The Impact of Omni-Channel Retailing on
Customer Satisfaction and the Moderation
Effect of Privacy Concerns
- What really matters for fashion retailers -
Laura van der Velde
S2747960
B170807326
l.j.van.der.velde@student.rug.nl
Dissertation
MSc Advanced International Business Management and Marketing
University of Groningen
Newcastle University Business School
Supervisors
Dr. H.U. Haq - University of Groningen
Dr. A. Javornik - Newcastle University Business School
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ABSTRACT
This study examines the impact of omni-channel retailing on customer satisfaction by using channel integration. The influence of six dimensions of channel integration on customer satisfaction is investigated, as well as their relative importance. In addition, the moderation effect of privacy concerns on the relationship between omni-channel retailing and customer satisfaction is examined. In order to explain the relationships between these constructs, this study is placed within a broader theoretical framework based on the expectance disconfirmation theory. The results are obtained by setting up a quantitative research design with a sample of 198 respondents, who have had a personal experience with using both an offline channel (physical store) and online channel (website and/or mobile device) of a fashion retailer. In line with the expectations, integrated brand promotion, integrated price information and integrated customer service are found to have a significant positive effect on customer satisfaction. However, contrary to the expectations, no significant effect was found of integrated assortment information, integrated order fulfilment and integrated customer data on customer satisfaction. Besides, the results showed no significant moderation effect of privacy concerns on the relationship between omni-channel retailing and customer satisfaction. Overall, the findings of this research suggest relevant academic and managerial implications, including the effectiveness of omni-channel retailing from the customer perspective, as well as practical guidelines for organisations towards the most successful strategy of omni-channel retailing.
Keywords: Omni-Channel Retailing, Channel Integration, Customer Satisfaction, Privacy
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ACKNOWLEDGEMENTS
This dissertation marks both the end of my Master Advanced International Business Management and Marketing at the University of Groningen and the Newcastle University Business School as an important stage of my life. Years of studying, meeting new people, developing myself both personally and professionally led to this day that I am writing my acknowledgements as the finishing touch of my dissertation.
First and foremost, I would like to thank my supervisors dr. H.U. Haq from the University of Groningen and dr. A. Javornik from the Newcastle University Business School for their valuable feedback, friendly support and fast responses during the development of this research. It was an honour and pleasure working with them during the final stage of my student life.
Furthermore, I would like to take this opportunity to thank everyone that participated in my survey and helped me distributing it. Without their support, I could not have completed my dissertation and I can only hope to return the favour one day.
Finally, I have to express my eternal gratitude to my family, friends and colleagues for their wise advice, encouragement and endless love. I made it to the point where I am standing now because of their support. It is impossible to describe how important they were to me throughout my study time and during my dissertation process.
Thank you all.
4 TABLE OF CONTENTS ABSTRACT ... 2 ACKNOWLEDGEMENTS ... 3 LIST OF TABLES ... 6 LIST OF FIGURES ... 6 1. INTRODUCTION ... 7 2. THEORETICAL BACKGROUND ... 13
2.1 Expectancy disconfirmation theory ... 13
2.2 From multi-channel retailing to omni-channel retailing ... 14
2.3 Channel integration ... 17
2.4 The relationship between channel integration and customer satisfaction ... 20
2.5 The moderation effect of privacy concerns ... 21
3. METHODOLOGY ... 26
3.1 Research design ... 26
3.2 Research context ... 26
3.3 Data collection and survey development ... 27
3.4 Sample selection and sample characteristics ... 29
3.5 Measurements ... 31
3.6 Control variables ... 32
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3.8 Ethical issues ... 34
4. RESULTS ... 35
4.1 Validity analyses ... 35
4.2 Reliability analyses and descriptive statistics ... 37
4.3 Hierarchical regression analyses... 37
5. DISCUSSION ... 42
5.1 Omni-channel retailing and customer satisfaction ... 42
5.2 The moderation effect of privacy concerns ... 45
5.3 Implications for the research question ... 46
6. CONCLUSION ... 47
6.1 Academic contributions ... 47
6.2 Managerial implications ... 48
6.3 Limitations and future research ... 49
7. REFERENCES ... 52
8. APPENDIX ... 64
Appendix A: Overview of the survey items and original scales used in the questionnaire.. 64
Appendix B: Overview of the retailers mentioned in the questionnaire ... 68
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LIST OF TABLES
Table 3.1: Sample characteristics………..31
Table 3.2: Overview of the original scales used in the questionnaire……….33
Table 4.1: Factor loadings from the factor analysis of channel integration………..36
Table 4.2: Factor loadings from the factor analysis of privacy concerns………..36
Table 1.3: Factor loadings from the factor analysis of customer satisfaction…………...36
Table 4.4: Descriptive statistics, correlations and reliability………..38
Table 4.5: Results from standardized multiple regression analyses………..39
Table 4.6: Overview of the results of the hypotheses………...41
Table 2.1: Overview of the survey items and original scales used in the questionnaire...63
Table 8.2: Retailers respondents had an omni-channel retailing experience with……...67
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1. INTRODUCTION
The rise of new technologies has revolutionized marketing and retailing. Digital devices such as smartphones, smart products, and artificial intelligence increased the complexity of retail business models and customer behaviour (Verhoef, Kannan and Inman, 2015). Driven by this technological revolution, large firms such as Disney, Starbucks and Walmart increasingly started adding new online channels to their existing offline channel mix. In recent years, however, the distinction between the online and the offline world vanished, which brings many challenges for retailers today (Brynjolfsson, Hu and Rahman, 2013). In literature, this phenomenon is known as the transition from single, to multi-, to omni-channel retailing.
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In academic literature, many research related to omni-channel retailing focused on the development from multi-channel retailing to omni-channel retailing conceptually (e.g. Beck and Rygl, 2015; Verhoef et al., 2015). Other studies discussed the effects of online channel additions and eliminations (e.g. Konus, Neslin and Verhoef, 2014; Homburg, Vollmayr and Hahn, 2014). Furthermore, researchers tried to develop an understanding of how firms can create an integrated retailing environment, such as for product availability (Bendoly et al., 2005), product assortment and information services (Emrich, Paul and Rudolph, 2015), product delivery (Bell, Gallino and Moreno, 2014) or product prices (Melis et al., 2016). Empirical research on the effectiveness of these omni-channel retailing strategies is however limited, but necessary (Kannan and Li, 2017).
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(Anderson, Fornell and Lehmann, 1994) and profitability (Mittal et al., 2005). Consequently, the first contribution of this research is to provide empirical evidence for the impact of omni-channel retailing on customer satisfaction.
omni-10
channel retailing strategies might be less beneficial and effective due to the privacy concerns of customers.
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customers and ensure that every channel can deliver up to its full capability (Zhang et al,, 2010). Therefore, the second research contribution of this study is to examine the moderation effect of privacy concerns on the relationship between omni-channelling retailing and customer satisfaction. Understanding the moderation effect of privacy concerns on the effectiveness of omni-channel retailing is also valuable for retailers, because it increases the likelihood of successful omni-channel retailing strategies.
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changing room in the correct sizes (Mintel Group Ltd, 2017b). This proves that omni-channel retailing is likely to progress much further in the fashion industry and is referred to as one of the most important business trends shaping the fashion industry’s future (The Business of Fashion and McKinsey & Company, 2017). Therefore, the fashion industry is chosen as subject for this research.
Overall, this study offers new contributions to the literature given the importance of the aforementioned unexplored aspects of omni-channel retailing. First, this research will examine the impact of omni-channel retailing on customer satisfaction. Therefore, this study will also provide empirical evidence of the relative importance of various channel integration dimensions. Second, as this research focuses on the customer perspective, it is important to take variables into account which could influence the effectiveness of their perceptions of omni-channel retailing. Therefore, the moderating effect of privacy concerns on the relationship between omni-channel retailing and customer satisfaction will be examined. Based on the previous topics presented, the following research question is proposed:
‘To what extent does omni-channel retailing have an impact on customer satisfaction, using privacy concerns as the moderating variable?’
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2. THEORETICAL BACKGROUND
2.1 Expectancy disconfirmation theory
In order to explain the impact of omni-channel retailing, expectancy disconfirmation theory will be used as a theoretical framework (Oliver, 1977; Oliver, 1980). This theory exists out of four main constructs (expectations, perceived performance, disconfirmation and satisfaction), and proposes that customer satisfaction can be explained by the positive or negative disconfirmation between customers’ expectations and perceived performance of products or services. Satisfaction is defined by Oliver (1997) as ‘a judgement that a product or service feature, or the product or service itself, provided (or is providing) a pleasurable level of consumption related fulfilment, including levels of under- or over-fulfilment.’ As presented by Oliver (1980), satisfaction can be conceptualised as the function of two main processes: the formation of performance expectations and the formation of disconfirmation of those expectations through performance comparison.
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actual performance is as expected, the discrepancy has zero valence. Subsequently, the relationship between satisfaction and disconfirmation is straightforward. For instance, if a product or service outperforms customer expectations, positive disconfirmation will occur, leading to customer satisfaction. Similarly, if a product or service fails to meet expectations, negative disconfirmation will happen, leading to customer dissatisfaction.
According to Montoya-Weiss, Voss and Grewal (2003) customer satisfaction is determined by the expected and perceived service quality performance of both online and traditional offline channels. Therefore, the theory can be applied to an omni-channel retailing environment. The relationship between disconfirmation of service quality performance and customers satisfaction can be also mitigated by customer characteristics such as privacy concerns due to the heterogeneous expectations, standards and needs of customers (Zboja, Laird and Bouchet, 2016). Hence, the theory can shed more light on the complexity of the relationship between channel integration and customer satisfaction, and the moderation effect of privacy concerns.
2.2 From multi-channel retailing to omni-channel retailing
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example, Pauwels and Neslin (2015) found that adding a physical store cannibalizes sales in the catalogue channel, without having an impact on the sales of the Internet channel. In another research, Avery et al. (2012) showed the importance of time, as the addition of a physical store decreased sales in the catalogue channel in the short term (channel cannibalization), but increased sales in the catalogue and Internet channel in the long term (channel synergies). Nevertheless, it is important to note that the experience per channel can differ significantly between channels in a multi-channel context.
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providing the possibility to order a product online in-store, retailers themselves will also be able to provide the demanded seamless experience.
In light of the presented developments in retailing, researchers tried to distinguish the concept of multi-channel retailing from omni-channel retailing conceptually. Several studies focused on positioning them on a continuum based on the level of channel integration (Hure et al., 2017; Cao and Li, 2015; Picot-Coupey, Huré and Piveteau, 2016). Compared to multi-channel retailing, omni-channel retailing can be positioned on the other end of this continuum. Whereas the multi-channel world mainly emphasizes the management, objectives and experience per channel, the omni-channel world considers cross-channel management, resulting in objectives and experiences over channels (Verhoef et al., 2015). Consequently, omni-channel retailing can be represented as fully integrated, resulting in optimal omni-channel journeys (Hure et al., 2017; Ailiwadi and Farris, 2017). In fully integrated transactions, a single sale can involve the use of multiple channels to perform different functions (Friedman and Furey, 1999). It is important to note that the information and products required for the sale must be consistent across all channels, with the exact same look and feel for all touchpoints (Shenkar et al., 2011).
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refer to omni-channel retailing as ‘the complete alignment of the different channels and touch points, resulting in an optimal brand-customer experience’. Overall, the definitions have in common that the integration quality within and across channels is invaluable to provide a seamless omni-channel experience. Therefore, the concept of omni-channel retailing will be used interchangeably with channel integration in this research. This is in line with the view of Sousa and Voss (2006) on omni-channel retailing, as they argue that integration quality signals a seamless customer experience delivered within and across channels. Subsequently, the next paragraph will provide a literature review regarding channel integration.
2.3 Channel integration
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makes it easier to segment customers on the basis of their needs (Bell et al., 2015). At the same time, Banerjee (2014) emphasized the importance of channel integration as a means toward broader organizational goals. In his research, multiple aspects of channel integration are identified (e.g. integration of content and processes) and their relative impact on organizational outcomes are examined.
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The literature review on channel integration illustrates that most of the research regarding the impact of channel integration from the customer-centric perspective has been qualitative, exploratory research. Previous studies have identified several relevant channel integration practices of omni-channel retailing, which are referred to in this study as the dimensions of channel integration. These dimensions are developed through conducting interviews with customers and focus groups, to provide assurance of the content validity. The main content of channel integration, existing of integrated brand promotion, integrated price information, integrated assortment information, integrated order fulfilment, integrated customer service and integrated customer data is summarized and further clarified below (Banerjee, 2014; Bell et al., 2014; Bendoly et al., 2005; Hure et al., 2017; Oh et al., 2012; Grewal et al., 2009).
1. Integrated brand promotion: refers to the practices which enable customers to find consistent brand information across channels. Awareness of one channel is enhanced through advertising and publicity of it on another channel. In addition, promotions have to be consistent in both offline and online channels (Oh et al., 2012).
2. Integrated price information: means that product prices and discounts are consistent in the various channels of a retailer. Confusion is reduced by ensuring that customers have access to consistent pricing information. Moreover, customers should be able to issue vouchers and gift coupons to reduce prices through all channels (Huré et al., 2017; Oh et al., 2012). 3. Integrated assortment information: emphasizes providing standard and consistent product
and assortment information. Customers should be provided with access to stock availability and inventory status through all channels. Additionally, customers should be able to search for products available in a specific channel through all other channels (Huré et al., 2017; Bendoly et al., 2005).
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this dimensions refers to the integration of all activities to complete a transaction across multiple channels (Bell et al., 2015; Oh et al., 2017).
5. Integrated customer service: means that a retailer has to provide consistent services across all channels. This also involves after-sales support for products in the channel of a customer’s preference, despite the channel of purchase. After-sales support includes practices related to returns, repairs, and exchange of products (Bendoly et al., 2005; Oh et al, 2012).
6. Integrated customer data: means using customer profiles, which allows retailers to suggest future recommendations and make personalized offers based on their historic consumption patterns. The collected online and offline transaction data is also made available for customers to get an overview of their purchase records (Oh et al., 2012).
2.4 The relationship between channel integration and customer satisfaction
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findings, it was found that the relative importance of channel integration also increases when customer experience with channel integration increases (Melis et al., 2017). This explains why traditional retailers are feeling the pressure of adapting an omni-channel retailing strategy, because innovative retailers increasingly started implementing successful omni-channel retailing strategies. For example, fashion retailer ZARA started experimenting with a range of in-store technology concepts to strengthen the omni-channel customer journey, such as self-service tills and digital screens in store. In response, fashion retailer H&M made a significant shift in their strategy by revealing plans to improve the omni-channel customer experience both online and offline. In this way, the firm aims to stay engaged with the digital oriented customer of today, as it is not enough in the new omni-channel world to focus on one aspect of the customer journey. It is therefore expected that if a retailer’s design of channel integration fails to meet customer expectations based on their pre-existing standards regarding channel integration, customers will be dissatisfied due to the occurrence of negative disconfirmation. On the other hand, if retailers provide high levels of channel integration which exceed customer expectations, positive disconfirmation will occur. Consequently, this perceived outperformance of the retailers channel integration design will lead to higher levels of customer satisfaction. Considering the arguments outset above, the following hypothesis will be tested:
H1: A higher level of perceived channel integration through (a) integrated brand
promotion, (b) integrated price information, (c) integrated assortment information, (d)
integrated order fulfilment, (e) integrated customer service, and (f) integrated
customer data positively affects customer satisfaction.
2.5 The moderation effect of privacy concerns
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of customers’ privacy concerns. Omni-channel retailers have to collect data and information, because customers are heterogeneous in their preferences regarding channels (Bell et al., 2015). Retailers use data analytics to understand these heterogeneous needs and values and to personalize their services and marketing efforts (Brynjolfsson et al., 2013; Inman and Nikolova, 2017). For example, some customers prefer to use their mobile devices to search for information, products and user reviews online while visiting a physical store, whereas others prefer to obtain information physically in-store while buying products online (Shenkar et al., 2011). In any way, retailers must design the omni-channel journey to be so compelling for customers, that once customers encounter it, they will never feel the need to consider competitors (Edelman and Singer, 2015). However, customers sacrifice a part of their privacy by providing the information that enables these experiences and customers can perceive it as a breach of privacy if retailers apply too much marketing ‘push’ (Piotrowicz and Cuthbertson, 2014). Accordingly, customer privacy has been defined by Eastclick et al. (2006) as a two-dimensional construct consisting of control over information dissemination and information use. In line with this definition, concerns over privacy arise from the collection and control of personal data by retailers, and the customers’ awareness of retailers’ data and privacy practices (Inman and Nikolova, 2017). Therefore, privacy concerns will be defined in this research as ‘a customer’s worry on his or her personal information, such as fear of improper access, unauthorized secondary use, errors, data collection, and fraud’ (Gao, Waechter and Bai, 2015).
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of the strong negative effect of privacy concerns on purchase intentions. This relationship is of importance for this study, because purchase intentions are strongly related to customer satisfaction (Taylor and Baker, 1994; Oliver, 1977; 1980). In addition, exploratory research emphasized that customers with strong privacy concerns have a general negative attitude to all forms of personalized communication resulting from channel integration (Martin, Borah and Palmatier, 2017). As such, customers with strong privacy concerns could be less satisfied with channel integration than customers with low privacy concerns. Moreover, Dolnicar and Jordaan (2007) proposed a segmentation-based approach for direct marketing communication, based on customers heterogeneity regarding privacy concerns. They acknowledged that privacy is an important issue that needs to be addressed by marketeers and that privacy concerns can result in major changes in customer behaviour.
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retailing, privacy concerns and customer outcomes interact. Relating to Oliver’s (1977, 1980) expectancy disconfirmation theory of satisfaction, it is likely that higher levels of privacy concerns will mitigate the positive disconfirmation effects of channel integration on customer satisfaction.
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disconfirmation is likely to be stronger for individuals with lower privacy concerns than for those individuals with higher levels of privacy concerns, which is presented in the conceptual model in figure 2.1. Accordingly, the following hypothesis is proposed to be tested:
H2: A higher level of privacy concerns negatively affects the relationship between
perceived channel integration and customer satisfaction.
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3. METHODOLOGY
3.1 Research design
To examine the relationships between channel integration, customer satisfaction and privacy concerns, a quantitative data study is set up to find support for the proposed hypotheses. A quantitative approach is chosen for this study, because a large body of research has focused on exploring omni-channel retailing conceptually and understanding determinants of customer behaviour, while research enhancing generalizability is scarce (e.g. Verhoef et al., 2015; Inman and Nikolova, 2017). Hence, previous studies identified multiple factors of importance for omni-channel retailing, but they did not empirically test the influence of these factors in a larger population. Therefore, a quantitative research method providing empirical evidence is needed to draw conclusions of a larger population (Blumberg et al. 2014). Rather than building new theory, this study will test and refine knowledge about the theoretical framework this study is placed in, Oliver’s (1977, 1980) expectancy disconfirmation theory. Accordingly, a quantitative research design fits best.
3.2 Research context
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transportation (Zhang et al., 2010). Second, the fashion industry is relevant due to the close relationship between online and offline channel use in this category (Pauwels and Neslin, 2015).
3.3 Data collection and survey development
Taken the quantitative research design into consideration, the data is collected by using an online (standardized) survey. The online survey tool, Qualtrics software, is used to structure the survey and generate the data. An online survey is chosen, because this research aims to test observations, given that previous studies were mostly of exploratory nature. As a survey can be used to test hypotheses, this data collection approach fits best. Besides, an online survey is an effective approach to reach a large amount of (geographically dispersed) participants with limited resources and time (Blumberg et al., 2014). Furthermore, the online self-completion survey allows respondents to fill the survey in at their preferred time, place and speed, which could contribute to the response rate of the survey (Bryman and Bell, 2015).
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After the qualitative interviews, a pre-test with 10 respondents (eight graduate students and two experts in the field of (marketing) research) was conducted to test the clarity of the items used in the online survey. The respondents were encouraged to identify and comment on unclear questions and/or scales. Some of the questions were reworded and the order of the questions has been slightly changed on the basis of feedback from the respondents. Furthermore, the survey was first designed in English and then translated to the Dutch language. To ensure the translation quality and equivalence measurements of the English and Dutch version, three professional translators have controlled the survey for differences in meaning. As a result of this, a few items were modified. Subsequently, a pilot test of 20 respondents was conducted to test the final survey, reducing the likelihood of unclear items even further. This resulted in the online survey that has been distributed, because no significant problems have emerged after the pilot study.
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To motivate the respondents to provide valid data, the aim of this research was communicated in the introduction of the survey as well as the possibility to receive a summary of the results. This is believed to ensure the respondents’ professional interest in and commitment to providing correct data (Oh et al., 2012). In addition, the likelihood of incomplete data is reduced by asking no sensitive questions, such as religion and income, and by emphasizing the confidentiality of the information the respondents provide (Malhotra, 2010). An advantage of the primary data collected is the control over the data and the assurance that the data contains a sufficient amount of details.
3.4 Sample selection and sample characteristics
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Table 3.1: Sample characteristics
Characteristic Item Frequency Percent
Gender Male 108 54.5
Female 90 45.5
Age ≤ 30 111 56.1
31 ≥ 87 43.9
Education Primary School
Lower General Secondary Education Higher General Secondary Education Pre-university Education
Intermediate Vocational Education University of Applied Sciences University 0 3 10 20 14 48 103 0 1.5 5.1 10.1 7.1 24.2 52 Dutch 177 89.4 Non-Dutch 21 10,6 Firm Size (Number of employees) Micro Enterprise (≤ 10) 16 8.1 Small Enterprise (11-50) 7 3.5
Medium Sized Enterprise (51-250) 6 3
Large Enterprise (251 ≥) 169 85.4
Physical Store Network
(Number of physical retail stores) ≤ 5 6-10 11-20 27 19 33 13.6 9.6 16.7 21 ≥ 119 60.1 Total Sample 198 100 3.5 Measurements
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provides more detailed information than a 5-point Likert Scale without giving a participant too many response options (Matell and Jacoby, 1971).
First, the construct of customer satisfaction was measured using the items adapted from existing literature. In this research, three items were used for measuring customer satisfaction from Gustafsson et al. (2005). The prior study already proved the reliability of the items. In line with this research, the items are partly based on expectancy disconfirmation. Second, the six dimensions of channel integration have been carefully selected by studying prior research. To measure the perceived channel integration, items developed by Oh et al. (2012) have been used for integrated brand promotion, integrated price information, integrated assortment information, integrated order fulfilment, integrated customer service and integrated customer data. Finally, the privacy concerns items are the same as those used in the study of Mostellar and Poddar (2017). The authors developed their items based on thoroughly reviewing literature and tested the items on reliability
3.6 Control variables
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variable within this study. Finally, firm size and physical store network are expected to influence the level of customer satisfaction, because a large retailer and/or retailer with a large chain store can have more resources to implement successful omni-channel retailing strategies (Oh, Teo and Sambamurthy, 2012). Consequently, respondents were asked to provide the name of a fashion retailer they had a personal experience with in the past year (Appendix B), after which the required data to measure the control variables was gathered by secondary research. In this research, firm sizes was coded as the log of the number of employees, and physical store network was represented as the log of the number of physical network stores in the Netherlands (Oh et al., 2012).
Table 3.2: Overview of the original scales used in the questionnaire
Question Variable Number of items
Example Source
1-4 Demographics n.a. Age, Gender, Education, Nationality 5 Retailer
characteristics
n.a. The retailer’s name 6.1-6.4 Integrated Brand
Promotion
4 ‘The retailer's brand name, slogan and logo are consistent both offline and online.’
Oh et al (2012)
7.1-7.3 Integrated Price Information
3 ‘Product prices are consistent in both the offline channel and online channel(s).’
Oh et al (2012) 8.1-8.5 Integrated
Assortment Information
5 ‘Information on stock availability is consistent in both the offline channel and online channel(s).’
Oh et al (2012)
9.1-9.4 Integrated Order Fulfilment
4 ‘The offline channel allows customers to self-collect their online purchases.’
Oh et al (2012) 10.1-10.4 Integrated
Customer Service
4 ‘The retailer accepts returns at its offline channel for purchases made through the retailer’s online channel(s).’
Oh et al (2012)
11.1-11.4 Integrated Customer Data
4 ‘The retailer keeps an integrated purchase history of customers’ online and offline purchases.’
Oh et al (2012)
12.1-12.6 Privacy Concerns
6 ‘I am concerned that the retailer may keep my private information in a non-secure manner.’
Mosteller and Poddar (2017) 13.1-13.3 Customer
Satisfaction
3 ‘Considering the overall experience, I am satisfied with the retailer.’
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3.7 Data analysis
The data collected via the online survey will be analysed by using IBM SPSS 25.0 software. First, the validity of the variables in the data set will be assessed by exploratory factor analyses. Second, a reliability analysis will be conducted to measure the internal consistency of the final set of scale items and descriptive statistics will be presented. After this, hierarchical multiple regression analysis will be used as a predictive analysis technique to test the hypotheses. The method is appropriate for the quantitative research design of this study, because the relationship between the constructs is based on prior observations. A multiple regression analyses is chosen instead of a bivariate regression analyses, because the construct of channel integration has been split in six different dimensions. This makes it possible to determine the impact of each dimension of channel integration on customer satisfaction and the relative importance of the various dimensions.
3.8 Ethical issues
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4. RESULTS
4.1 Validity analysis
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Table 4.1: Factor loadings from the factor analysis of channel integration
Integrated Brand Promotion (IPB) Integrated Price Information (IPI) Integrated Assortment Information (IAI) Integrated Order Fulfilment (IOF) Integrated Customer Service (ICS) Integrated Customer Data (ICD) IPB1 0.787 0.108 -0.012 0.122 0.001 -0.067 IPB2 0.670 0.351 0.079 -0.076 -0.056 0.161 IPB3 0.521 0.069 0.044 0.064 0.119 0.177 IPB4 0.628 -0.091 0.160 0.028 0.148 -0.032 IPI1 0.239 0.841 -0.011 0.071 0.065 -0.004 IPI2 0.041 0.873 0.111 0.126 0.084 0.141 IAI3 0.039 0.407 0.638 0.122 0.048 0.157 IAI4 0.173 -0.016 0.826 0.102 0.070 0.096 IAI5 0.060 -0.024 0.834 0.241 0.061 0.050 IOF1 0.161 0.042 0.221 0.699 0.040 0.156 IOF2 0.156 0.095 -0.089 0.774 0.096 0.077 IOF3 -0.135 0.266 0.195 0.656 0.064 0.172 IOF4 -0.012 -0.079 0.238 0.660 0.142 0.121 ICS1 0.104 0.061 -0.023 0.138 0.867 0.107 ICS2 0.091 0.051 0.296 0.156 0.655 -0.024 ICS3 0.062 0.048 -0.023 0.025 0.885 0.097 ICD1 0.056 0.014 0.176 -0.009 0.182 0.753 ICD2 -0.066 0.013 0.095 0.212 0.183 0.786 ICD3 0.128 0.098 0.035 0.186 -0.032 0.825 ICD4 0.092 0.091 -0.010 0.127 -0.092 0.805
Bold numbers indicate that the measures loaded on the factor
Table 4.2: Factor loadings from the factor analysis of privacy concerns
Privacy Concerns PC1 .822 PC2 .843 PC3 .887 PC4 .890 PC5 .884 PC6 .768
Bold numbers indicate that the measures loaded on the factor
Table 4.3: Factor loadings from the factor analysis of customer satisfaction
Customer Satisfaction
CS1 .878
CS2 .893
CS3 .817
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4.2 Reliability analyses and descriptive statistics
The reliability analyses measure the internal consistency of the final set of scale items by testing for the Cronbach’s alpha. The Cronbach’s alpha is a measurement that indicates the degree to which the items that are supposed to measure one underlying concept are consistent with each other. Reliable results are reached when all the items related to a factor measure the same concept. The items capture a concept satisfactory, if the Cronbach’s alpha for each factor is higher than 0.6 (Malhotra, 2010). For the concept of customer satisfaction a Cronbach’s alpha of 0.798 was found, for integrated brand promotion a Cronbach’s alpha of 0.607 was found, a Cronbach’s alpha of 0.800 belongs to integrated price information and a Cronbach’s alpha of 0,762 was found for integrated assortment information. In addition, integrated order fulfilment has a Cronbach alpha of 0,724, a Cronbach’s alpha of 0.778 was found for integrated customer service, a Cronbach’s alpha of 0,831 was measured for integrated customer data and for the concept of privacy concerns a Cronbach’s alpha of 0,923 was found. Hence, all Cronbach’s alphas are between 0,607 and 0,923, meaning that the items are internally consistent. Consequently, a sum variable was created for all constructs by calculating the average scores of the items loading high on its respective factor. These eight new averaged variables are formed to be used for further analyses. Table 4.4 presents an overview of the descriptive statistics of the different variables, including the Cronbach’s alphas of each variable and correlations between the variables.
4.3 Hierarchical regression analysis
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overview of the results of the three hierarchical regressions for customer satisfaction. Model 1 includes the four control variables, age, gender, firm size and physical store network, and has an R-square of 0.029 and a non-significant F statistic (1.434). Model 2 adds the six dimensions of channel integration: integrated brand promotion, integrated price information, integrated assortment information, integrated order fulfilment, integrated customer service and integrated customer data. The R-square of the model is 0.234 and the F statistic (5,716) is significant at a confidence level of 99%. Finally, model 3 includes all the previously mentioned variables and adds the moderation effect of privacy concerns. The model has an R-square of 0.277, meaning that the model explains a relatively large part of the variability of the response data. In addition, the F statistic (4,336) of model 3 is also significant at the 99% confidence level and all the Variance Inflation Factors (VIF) values lie between 1 and 10, meaning that there is no multicollinearity (O’Brien, 2007).
Table 4.4: Descriptive statistics, correlations and reliability
Mean (standard deviation) 1 2 3 4 5 6 7 8 1. CS 5.5707 (0.83379) 0.798 2. IBP 5.5896 (0.91753) 0.268** 0.607 3. IPI 5.2348 (1.45374) 0.319** 0.305** 0.800 4. IAI 4.6852 (1.42660) 0.242** 0.238** 0.217** 0.762 5. IOF 4.5833 (1.30014) 0.216** 0.173* 0.241** 0.403** 0.724 6. ICS 5.1364 (1.26023) 0.310** 0.196** 0.165* 0.215** 0.272** 0.778 7. ICD 4.3182 (1.35922) 0.152* 0.179* 0.190** 0.251** 0.356** 0.184** 0.831 8. PC 3.7870 (1.41858) -0.388** -0.100 -0.121 -0.039 -0.033 -0.107 0.140* 0.923
Cursive numbers indicate the Cronbach’s alpha for the corresponding factor ** Significant at p < 0.01
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Table 4.5: Results from standardized multiple regression analyses
Model 1 Model 2 Model 3
coefficient estimate standard error (VIF) coefficient estimate standard error (VIF) coefficient estimate standard error (VIF) Control Gender -0.113 0.126 (1.133) -0.030 0.118 (1.215) -0.043 0.118 (1.247) Age -0.031 0.005 (1.209) -0.017 0.004 (1.337) 0.018 0.005 (1.384) Firm Size -0.148 0.074 (1.215) -0.192** 0.069 (1.309) -0.144 0.070 (1.380) Physical Store Network 0.043 0.001
(1.140) 0.032 0.001 (1.247) 0.037 0.001 (1.286) Main effects
Integrated brand promotion 0.154* 0.060 (1.279)
0.156* 0.061 (1.339) Integrated Price Information 0.196** 0.059
1.237)
0.182* 0.059 (1.266) Integrated Assortment Information 0.080 0.062
(1.336)
0.108 0.064 (1.478) Integrated Order Fulfilment 0.060 0.063
(1.389)
0.085 0.066 (1.567) Integrated Customer Service 0.203** 0.058
(1.167)
0.219** 0.058 (1.207) Integrated Customer Data 0.050 0.061
(1.323) 0.017 0.062 (1.383) Moderation effects IBP x PC 0.058 0.061 (1.241) IPI x PC 0.091 0.058 (1.218) IAI x PC -0.150 0.066 (1.455) IOF x PC 0.035 0.054 (1.556) ICS x PC 0.101 0.056 (1.389) ICD x PC 0.099 0.055 (1.426) F value 1.434 0.029 0.009 5.716** 0.234 0.193 4.336** 0.277 0.213 R2 Adjusted R2
Dependent Variable: Customer Satisfaction; N = 198 ** Significant at p < 0,01
* Significant at p < 0,05
40
have a significant and positive effect on the dependant variable (β = 0.182, p < 0.05). This result suggests that if a retailer’s price information is perceived as more integrated across channels, customers are more satisfied with the retailer. Therefore, hypothesis 1b is empirically supported. Third, empirical support for hypothesis 1c was not found. Integrated assortment information does not have a positive, nor negative significant effect on customer satisfaction (β = 0.108, p > 0.05). In addition, a significant effect of integrated order fulfilment on customer satisfaction was not found (β = 0.085, p > 0.05). Hence, hypothesis 1d is not supported. Furthermore, a positive and significant effect of integrated customer service on customer satisfaction was found (β = 0.219, p < 0.01). Besides that, the value of the beta coefficient is the highest for integrated customer service, suggesting that the effect of integrated customer service on customer satisfaction is stronger than each of the other independent variables. Retailers who sufficiently integrated their customer services, are more likely to have satisfied customers. Moreover, an insignificant effect of integrated customer data on customer satisfaction was found (β = 0.017, p > 0.05). Therefore, as not was predicted by hypothesis 1f, a higher level of integrated customer data does not positively affect customer satisfaction. Finally, empirical support for hypothesis 2 was not found. Privacy concerns do not have a positive, nor negative significant effect on the relationship between any of the dimensions of channel integration and customer satisfaction (β = 0.108, p > 0.05).
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results of the hypotheses. In sum, it can be concluded that hypothesis 1a, hypothesis 1b, and hypothesis 1e are supported, while hypothesis 1c, hypothesis 1d, hypothesis 1f, and hypothesis 2 are not supported.
Table 4.6: Overview of the results of the hypotheses
Hypotheses Results
H1a: A higher level of integrated brand promotion positively affects customer satisfaction.
Supported
H1b: A higher level of integrated price information positively affects customer satisfaction.
Supported
H1c: A higher level of integrated assortment information positively affects customer satisfaction.
Not supported
H1d: A higher level of integrated order fulfilment positively affects customer satisfaction.
H1e: A higher level of integrated customer service positively affects customer satisfaction.
H1f: A higher level of integrated customer data positively affects customer satisfaction.
H2: A higher level of privacy concerns negatively affects the relationship between all dimensions of channel integration and customer satisfaction.
Not supported
Supported
Not supported
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5. DISCUSSION
5.1 Omni-channel retailing and customer satisfaction
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Next, besides that a positive significant relation was found between three of the six dimensions of omni-channel retailing and customer satisfaction, it was also found that integrated customer service has the strongest impact on customer satisfaction. Thus, integrated customer service is suggested as the predominant factor which causes customer satisfaction. The logic behind the argumentation was again based on, and in consonance with, the second stage of expectancy disconfirmation theory of Oliver (1977, 1980). Customers who perceive the level of integrated customer service as higher than expected, are likely to be more satisfied. A possible explanation for the importance of this dimension in particular can be found in the study of Verhoef et al. (2015). The scholars have already stated that interacting and moving across channels without service failures have become the new norm. Subsequently, this new norm is manifested through the formation of positive disconfirmation for retailers who outperform customer expectations regarding integrated customer service. In addition, the importance of integrated customer service is also acknowledged in more depth by other research. For instance, Cao and Li (2015) suggested that value-added integrated customer service might be the mechanism through which customers reward retailers with a higher level of satisfaction. The results confirm that retailers need to look at their firm as a holistic system, giving customers the option to use online and offline channels interchangeably, including the provision of various customer services such as exchanges, repairs and returns. This is especially relevant in the fashion industry, because the inability to try products ahead of purchase, and understanding the product quality and sizes are the biggest barriers for consumers to entry online (Mintel Group Ltd, 2017a). Integrating customer service across channels could eliminate these problems, which significantly improves customer satisfaction.
44
45
but as it is a common practice among retailers today, it does not longer result in positive confirmation by consumers.
5.2 The moderation effect of privacy concerns
46
strategies. Therefore, their privacy concerns do not have an impact on the relationship between channel integration and customer satisfaction. Overall, it can be concluded that despite the high or low privacy concerns of customers, expectations regarding channel integration and the retailer’s performance remain the most important. This confirms that fashion retailers are right to be experimenting with technologies such as digital screens in-store, artificial intelligence and chatbots for customer service online.
5.3 Implications for the research question
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6. CONCLUSION
6.1 Academic contributions
In light of the transition in retailing from single, to multi-, to omni-channel retailing, this study contributes to academic literature whereas it is the first study, to the best of the author’s knowledge, to provide empirical evidence for the impact of omni-channel retailing on customer satisfaction. The study and its results are placed within a broader theoretical framework based on the expectancy disconfirmation theory (Oliver, 1977; 1980). It appears that omni-channel retailing partly determines customer satisfaction. Apparently, customers hold different expectations regarding the various dimensions of channel integration. These different expectations explain why investing in some dimensions of channel integration do receive a payoff in terms of customer satisfaction, while other dimensions do not. Three dimensions of omni-channel retailing, integrated brand promotion, integrated price information and integrated customer service, have a positive effect on customer satisfaction, whereas integrated assortment information, integrated order fulfilment and integrated customer data are found not to have an effect on customer satisfaction. Besides that, it has been shown that integrated customer service has the strongest effect.
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6.2 Managerial implications
This study also provides important implications for practitioners besides its academic contributions. First, this study intents to guide organisations towards the most successful strategy regarding omni-channel retailing. By enriching the understanding of the effectiveness of six dimensions of omni-channel retailing, a point of focus is created for managers. Thereby, this study help firms by providing more confidence in investments into omni-channelling, because the findings show which dimensions are particularly rewarding in terms of customer satisfaction.. A future study could be helpful for managers to gain more specific insights about which omni-channel strategy is the most effective for specific customer segments.
Second, because previous studies mainly focused on the perspective from the firm, this study helps managers to evaluate the impact of their omni-channel strategies on customers. By paying attention on the effect of omni-channel retailing on customer satisfaction, their investments into omni-channel retailing are likely to become more beneficial.
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6.3 Limitations and future research
This study has its strengths, but is also subject to several limitations. Therefore, the following limitations have to be taken into account when interpreting the results and findings, but also present opportunities and suggestions for future research. First, to measure the retailers’ strategies regarding omni-channel retailing, customer perceived channel integration was used of a retailer’s offline channel (the physical store) and the retailer’s online channels (the website and/or mobile devices). However, customers were solely asked to provide the name of the retailer they had a recent omni-channel experience with, without having to provide any further details about the experience. Possibly, there is a variance in the experience of customers affecting the results, and customers might not always be aware of the omni-channel activities performed by a retailer. Therefore, further research is needed to assess whether the results of this study hold if more data is collected. It would be worthwhile to ask respondents in more detail about their experiences and knowledge regarding a retailer’s omni-channel activities.
Second, a distinction was made between online and offline channels within this study. However, both online and offline channels include a large variety of channels, and the perceptions of customers might be influenced by a more specific retail channel mix. Therefore, a replication of this study in more detail by examining the perceived channel integration of other retail channels (e.g. pop-up stores, kiosks, or social media) is necessary for validation and extension of the findings.
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(2010) argue that a retailer’s omni-channel strategies are significantly simpler for service retailers as for merchandise retailers. Further work should address how the relationships among channel integration, privacy concerns and customer satisfaction varies between different industries and product or service categories.
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Fifth, the data was collected by using convenience sampling due to a lack of resources and a limited amount of time available to collect data. However, as a result of the convenience sampling, most respondents were from the same age group and have had an experience with a large retailer. This has consequences for the generalizability of the results, because the sample could possibly not be representative for the entire population. Also, the low level of diversity in the sample make the results not very robust, as a comparison between the groups is difficult. An additional sample with a higher level of diversity is needed to provide clearer insights.
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8. APPENDIX
Appendix A: Overview of the survey items and original scales used in the questionnaire
Table 8.1: Overview of the survey items and original scales used in the questionnaire
Variable Survey items Source
Control Variables C1: What is your gender? C2: What is your age?
C3: What is your nationality?
C4: What is your highest completed level of education?
C5: Please think about your personal experience with a retailer, where you used
both an offline channel and an offline channel during the past year. Using the retailer’s offline channel refers to you visiting a physical store, using the retailer’s online channels refers to you visiting the retailer’s online website and/or you using your mobile device.
Provide the name of the retailer you had the experience with.
n.a. n.a. n.a. n.a. n.a.
Please answer the upcoming questions to the best of your knowledge with the previous mentioned retailer in your mind.
Please indicate your level of agreement with the following statements, by using the provided scale (1= strongly disagree, 7= strongly agree).
Integrated Brand Promotion (IPB)
IPB1: The retailer's brand name, slogan and logo are consistent both offline and
online.
IPB2: The retailer advertises promotions that are taking place in the offline
channel on its online channel(s).
