determinants of omnichannel customer experience: a growing

Journal of Business Studies, Vol. XL, No. 3, December 2019

Determinants of Omnichannel Customer Experience: A Growing Digital Economy Perspective

Muhammad Intisar Alam* Mohammad Osman Gani

Abstract: In recent decades, the retailing pattern has changed through the development of the internet and information technology (IT). The omnichannel retailing strategy enables an authentic interaction with the customer who purchases from channels anywhere and at any time while being provided with the ultimate customer experience. This study aims to assess customers’ omnichannel retailing experience and behaviour in Bangladesh, and to discover factors affecting their satisfaction, loyalty intention and word-of-mouth behaviour. Our analysis was conducted with a sample of 203 respondents from different groups of people accustomed to omnichannel purchasing. Data were collected by the online survey method using social media platforms through snowball and convenience sampling. Factors, such as ease of use and customisation, were found to have a significant influence on customer experience, whereas convenience’s influence was insignificant. Customer experience was found to work as a mediating variable positively and significantly affecting customer satisfaction, loyalty intention and word of mouth. The measurement model was tested using partial least squares (PLS) and confirmatory factor analysis (CFA) to confirm the model’s validity. Structural equation modelling (SEM) and CFA were conducted to identify relationships among the variables. The findings provide key managerial implications for businesses, entrepreneurs and policy makers from gaining insight into what customers expect in their omnichannel experience from a growing digital economy perspective.

Keywords: Omnichannel, retailing, satisfaction, loyalty intention, word of mouth.

1. Introduction

Technological innovation has had a tremendous effect on customers’ purchase behaviour (Fogg, 2002). The enhancement of new technologies, such as the internet, smartphones and social network platforms, as well as developments of in-store technological arrangements have created new opportunities, opening the gateway for customers to access retail offerings (Duncombe, 2014). Technological aspects are also changing the attitudes and behaviour of customers (Toufaily et al., 2013). More customers are increasingly using two or more channels together for their shopping venture: these

*Assistant Professor, Department of Marketing, University of Dhaka, Dhaka-1000 Assistant Professor, Department of Marketing, Faculty of Business Studies, Bangladesh University of Professionals (BUP), Email: [email protected]

204 Journal of Business Studies, Vol. XL, No. 3, December 2019

customers, called “omni-shoppers”, would like a smoother experience through the channels (Verhoef et al., 2015). The omnichannel concept combines use of the web with a physical channel, conveying a consistent customer experience, while paying little attention to which channel is used (Picot-Coupey et al., 2016). For instance, a customer can browse for a product through a mobile phone application, compare the product’s price with other retailers online, and buy the product from the physical store. Customers may even search for the product, compare product prices and check the product’s availability online when they are at the store (Burke, 2002). In the omnichannel environment, channels are utilised consistently and conversely when hunting for and buying products, with retailers finding it troublesome to control customers’ utilisation of the internet (Verhoef et al., 2015). Omnichannel-based customer experience consists of touch points for the individual over a compilation of channels that flawlessly interface, enabling the customer to move from where they signed off on one channel to proceeding with the experience on another channel. This can serve the customer better than a multichannel process.

The omnichannel concept is regarded as an updated form of multichannel retailing. While the latter refers to several divisions, for example, the physical store and online shop, in the omnichannel environment, customers can move among channels (on the web, mobile phones and physical shop) all of which are within a solitary exchange process (Zhang et al., 2010). The Latin origin word omnis means “all”, with the term “omnichannel” meaning “every channel together” (Lazaris et al., 2014). As every channel is simultaneously overseen, the apparent association does not remain with a single channel (Piotrowicz & Cuthbertson, 2014). Retailers in several countries are at various stages of accomplishing omnichannel capability. In Western Europe and North America, retailers are rapidly adjusting to rising customer interest in omnichannel shopping encounters. Almost 70% of United States (US) smartphone users shop online or compare prices using their smartphones. Retailers in the Asia Pacific and Latin America are attempting to improve their omnichannel contributions by addressing the issue of secure instalments while seeking to persuade customers of the advantages of shopping through numerous channels. In the United Kingdom (UK), Australia, France, Germany, Austria and Canada, omnichannel retailing is generating an enormous amount of competition. In the results of a BigCommerce survey, 78% of respondents worldwide bought something on Amazon, 65% had visited a physical store, while 45% had accessed renowned online stores, with eBay being second from the bottom at 34% and 11% citing Facebook (Fontanella, 2020). Many companies worldwide, such as Disney, Starbucks, Walgreens, Sephora, Amazon, Virgin Atlantic, etc., have implemented brilliant omnichannel customer experiences and are establishing themselves as successful omnichannel retailers. However, many countries around the world are still in the introductory phase of the omnichannel retailing process.

Determinants of Omnichannel Customer Experience: A Growing Digital Economy 205

Keeping pace with modern business style, the omnichannel concept is emerging from infancy in Bangladesh. As a developing country, Bangladesh has some factors that are influencing customers to adopt omnichannel retailing. The Bangladesh government and private organisations are facilitating usage of the internet, with internet facilities utilised by customers to buy their daily necessities. In 2011, 5% of the population actively utilised the internet with there being approximately eight million internet connections (Bangladesh Telecommunication Regulatory Commission [BTRC], 2018). In May 2019, the number of users stood at 94.445 million (BTRC, 2018). Online business in Bangladesh has prospered through the internet and, more specifically, e-retailing. Bangladesh Bank has allowed its masses of customers to use online transactions, with a simpler instalment framework from 2009. This strategy quickly and significantly increased online exchanges from 6.5–18.02% between 2009 and 2017 (Bangladesh Bank, 2018). Money mobility has smoothly facilitated business transactions. Other than the internet, customers are leaning towards different types of gadgets like smartphones, personal computers (PCs), cars and modern e-retailing tools. In Bangladesh, Bata offers its customers omnichannel tools, while Aarong’s customers can select products via its website and apps and purchase them from any retail store. Many other retail shops, such as Banglashoppers and The Mall sell cosmetics through physical stores, by phone, social media, mobile phone and website.

In seeking to measure customer experience in omnichannel retailing in Bangladesh, we have observed that people perceive ease of use, convenience and customisation as significant factors in their experience of retailing (Rose et al., 2011). Customer satisfaction then creates referrals through loyalty and positive word of mouth which helps retailers to achieve success in this field (Klaus & Maklan, 2012). Most previous studies have been on the strategic development of omnichannel retailing (Verhoef et al., 2015), with the literature on the omnichannel customer experience having received little attention, until now, from researchers. Research is particularly scarce on the determinants that influence the customer experience in the omnichannel retail context. Therefore, this study’s objective is to examine the different drivers that influence the omnichannel customer experience based on the outcome focus. In addition, the study seeks to find answers to the following research questions: what factor affects different factors in measuring the customer experience of using the omnichannel system, and what are the relationships among customer experience and customer satisfaction, loyalty intention and word of mouth?

2. Literature Review

The omnichannel system has a broad range of points of view on the various channels, examining how customers are travelling within the channels, according to their preferences and purchasing activities (Melacini et al., 2018). Retail business is advancing


towards the omnichannel, with customers to experience the disappearance of brick-and-mortar and online stores (Drabik & Zamecnik, 2016). Retail business will concentrate on helping customers, instead of focusing on exchanges and transactions as it did before. As stated by Kamel and Kay (2011), a pure omnichannel retail system wants to serve its customers at any point from any place where they wish to buy their merchandise. A customer can buy a product on the web (a computerised condition) and can gather the product information at the provider’s outlet. Establishing a pure omnichannel retailing strategy infers a mix of in-store and internet-based customer shopping behaviour (Kim and Jones, 2009). Omnichannel retailing depends on several factors as found in several studies that are discussed next (Rodríguez-Torrico et al., 2017).

2.1 Theoretical Background and Hypotheses Development

Technology acceptance model (TAM)

The adoption of new technology and innovation, including the perception of innovation, underlies multiple models (Silva et al., 2019). The technology acceptance model (TAM) is widely accepted (Davis et al., 1989) and a precursor, claiming that the perceptions or beliefs on innovation are crucial in developing the attitudes which eventually lead to adoption (Agarwal & Prasad, 1998). The TAM postulates that adoption intention is found within the intention to adopt a specific technology, vis-à-vis its determined perceived usefulness and perceived ease of use (Venkatesh, 2000; Silva et al., 2019). The variables used in that study are widely studied and applied to explain the determinants of omnichannel customer experience (Silva et al., 2019; Arora and Sahney, 2018). Williams et al. (2015) explained that, although plentiful research is published on technology adoption behaviour model-based research, such as the TAM and the unified theory of acceptance and use of technology (UTAUT), from the perspective of different geographic regions, most are unsurprisingly conducted in the developed country context. Due to the unique nature of services, the current study utilises the TAM from the perspective of Bangladesh as a developing country.

2.2 Variables Influencing Omnichannel Customer Experience

Providing a better customer experience is the critical target in the retail situation, whether store-based (Verhoef et al., 2009) or internet-based (Herhausen et al., 2015). Omnichannel retailing depends on various factors or drivers that comprise a seamless consistent customer experience (Shen et al., 2018). Customers can begin their adventure at any place, at any time and through any device (Joy et al., 2009). If they stop halfway, they also expect coordination to get from where they left off to where they are going through another gadget or channel (Mena et al., 2016). Therefore, understanding the variables that influence the omnichannel customer experience may include others, such as ease of use, convenience, customisation, etc. (Ha & Stoel, 2009). Shopping for a product


or service increases loyalty and satisfaction and tends to create a splendid customer experience overall toward the brand.

Ease of use

The ease of use of technological devices and capacities influences customer involvement in omnichannel retailing. Ease of use means the capability through which customers can find out the basic technical functions and capabilities. As stated by Rose et al. (2011), ease of use is a significant factor fit affecting customer feelings inside the online retailing condition. The theory of reasoned action (TRA) shows the significant association between ease of use and behavioural intention (Evans et al., 2014). Several studies have provided evidence of the relationship between ease of use and customer experience (Rose et al., 2011; Gefen, 2003). Furthermore, the ease of use of a mechanical framework depends on how it enables the customer to action tasks, enhancing the generation of goods, the execution and the effectiveness (Chau & Lai, 2003). Therefore, we hypothesise the following:

H1: Ease of use has a positive relationship with customer experience (outcome focus)

Customisation

To improve the ease of exercise of a technology-based function, Bilgihan et al. (2016) recommended that a useful factor is customisation of the product so it is suited to the customer experience. Customers know the information they have provided to firms about their purchase experiences and habits, and they want something in return. They want firms to understand them, as well as understanding what they want and through which channel. Offering customers the chance to filter content, their favourite content and relevant content to their desired outcome is part of positive customer expectations (Rose et al., 2011). We therefore hypothesise the following:

H2: Customisation has a positive relationship with customer experience (outcome focus)

Convenience

Retailers can enhance customer value by accelerating convenience, ensuring access from anywhere and being responsive to customer orders (Chopra, 2016). If omnichannel customers can access information any time and anywhere, and can check product or service availability, order the product or make a purchase from the physical store while simultaneously comparing it with other brands, it can be said that the business is convenient to customers (Gao & Su, 2016). This positively reinforces the customer experience (Srivastava & Kaul, 2014), whereas their experience may be negatively affected by lack of convenience by omnichannel retailers (Seiders et al., 2005). As stated by Taylor and Levin (2014), customers can or should be aware of the time taken by the action; therefore, the capacity to seamlessly finish the purchasing exercise may prompt a


successful customer experience inside this unique situation. When customers use an app in a hurry, frequently when seeking the firm’s help, the period he/she invests in using the app should be convenient (Taylor & Levin, 2014). Therefore, we hypothesise the following:

H3: Convenience has a positive relationship with customer experience (outcome focus)

Customer experience

Klaus and Maklan (2012) indicated that customer experience is an outcome focus that measures the total customer experience. Outcome focus is concerned with how the customer experience relates to important marketing outcomes (Klaus & Maklan, 2013). Customer experience, as the outcome focus, plays a mediating role between ease of use, convenience and customisation and customer satisfaction, loyalty intention and word-of-mouth behaviour. These measures help to identify the total scenario of potential buyer behaviours which may be triggered by customer experience (Mascarenhas et al., 2006).

2.3 Relationship of Customer Experience (Outcome Focus) and Customer Satisfaction, Loyalty and Word of Mouth

Customer experience is the main factor in customer loyalty and satisfaction (Caruana, 2002). As recommended by researchers, the use of something drives satisfaction, with this converting to loyalty (Shankar et al., 2003). Marketing specialists have recognised the bridge between two factors: satisfaction and loyalty intentions (Yi & La, 2004). Satisfaction from using a product will make customers idolise that brand, with this idolisation directing customers to re-visit the outlet, and they may repurchase the same brand. Repurchasing will, in turn, create loyalty (Shobeiri et al., 2015; Verhoef et al., 2009). Regardless of the channel, whether it is store-based, internet or a mobile app, customers will always seek a good experience. Their use of the channel may be great, awful, apathetic, etc. and happens at whatever point they purchase an item or experience administration by a seller (Berry et al., 2002).

Mowen and Minor (2001) reported that customer satisfaction can be perceived through the attitude shown in users’ evaluation after purchasing a product or service. Satisfaction means an individual’s feeling of pleasure or disappointment in the product, when using it or expressing one’s desire for it (Kotler, 2000), whereas dissatisfaction means an unpleasant feeling about the action. As defined by van der Wiele et al. (2002), satisfaction is the positive and emotive behaviour of a person which appraises all aspects of each party. A business should always try to maintain its positive gestures with its buyers; however, at times, a few buyer outcomes may be unfulfilled (Kotler, 2000). Zins (2001) mentioned that when customers have a high level of satisfaction, this may lead to greater loyalty. De Wulf et al. (2003) defined the magnitude of the consumption of a product as loyalty, with this shown by the frequency of purchase by a user in a specific


organisation. Sheth and Mittal (2004) defined loyalty as a very broad attitude created by the dedication of an individual to a brand or a shop or a supplier. Kotler and Keller (2006) provided the following three aspects for customer loyalty: (1) repeat purchase (dedication to buy a product again); (2) retention (defending against negative influences); (3) referrals (offering referrals to new customers on behalf of the organisation).

Moreover, customer experience (outcome focus) not only influences customers’ loyalty (Anderson & Mittal, 2000) and satisfaction (Fornell et al., 2006), but also their use of word of mouth (Keiningham et al., 2007). Significant behavioural change may occur in purchasing by receiving information from referrals via word of mouth (Park et al., 2007). Word-of-mouth communication is a good medium for gaining credible information with the information collector coming face-to-face with some parties, and often receiving explicit information from other parties (Berger & Iyengar, 2013). Word of mouth has two important determinants: satisfaction and dissatisfaction (Wirtz & Chew, 2002), with purchase of the product the possible outcome of word of mouth.

Lastly, the customer has a perception about the service they will receive, judging this by how services are delivered to them. Prior research does not consider what is being delivered (Goldstein et al., 2002) nor by what measures the customer relates the experience about the delivery as having important marketing outcomes (Klaus & Maklan, 2012). The current research defines the gaps by addressing evidence that determined the factors affecting omnichannel customer expectations (or customer outcome focus) and their influence on customer behaviour, satisfaction, loyalty intention and word of mouth. We therefore hypothesise the following:

H4: Outcome focus has a positive relationship with customer satisfaction

H5: Outcome focus has a positive relationship with loyalty intention

H6: Outcome focus has a positive relationship with word of mouth

H7: Customer satisfaction has a positive relationship with loyalty intention

H8: Customer satisfaction has a positive relationship with word of mouth

2.4 Mediation Relationships

The current study attempts to establish mediation relationships by utilising two variables: customer experience and customer satisfaction (Klaus & Maklan, 2013). The use of customer experience as a mediating variable was evident in several studies (Klaus & Maklan, 2013; Srivastava & Kaul, 2014; Nasermoadeli et al., 2013). Srivastava and Kaul (2014) have shown the significant mediating effect of customer experience on the convenience and customer satisfaction relationship, with Nasermoadeli et al. (2013) also using customer experience as a mediating variable between different types of experience and customer satisfaction. In the current study, we have also used customer satisfaction


as a mediating variable (Klaus & Maklan, 2013; Bontis et al., 2007). Klaus and Maklan (2013) found customer satisfaction to be a mediating variable which positively affected customer experience and loyalty intention as well as customer experience and word of mouth. Based on that previous study, the current study intends to depict all direct and indirect mediation relationships among the variables. Thus, the study hypothesises that:

H9: Customer experience mediates between (i) ease of use and loyalty intention

(ii) ease of use and customer satisfaction

(iii) ease of use and word of mouth

H10: Customer experience mediates between (i) convenience and loyalty intention

(ii) convenience and customer satisfaction

(iii) convenience and word of mouth

H11: Customer experience mediates between (i) customisation and loyalty intention

(ii) customisation and customer satisfaction

(iii) customisation and word of mouth

H12: (i) The relationship between ease of use and customer loyalty is mediated by customer experience (outcome focus) and customer satisfaction

(ii) The relationship between ease of use and word of mouth is mediated by customer experience (outcome focus) and customer satisfaction

H13: (i) The relationship between convenience and customer loyalty is mediated by customer experience (outcome focus) and customer satisfaction

(ii) The relationship between convenience and word of mouth is mediated by customer experience (outcome focus) and customer satisfaction

H14: (i) The relationship between customisation and customer loyalty is mediated by customer experience (outcome focus) and customer satisfaction

(ii) The relationship between customisation and word of mouth is mediated by customer experience (outcome focus) and customer satisfaction

H15: (i) The relationship between customer experience and loyalty intention is mediated by customer satisfaction

(ii) The relationship between customer experience and word of mouth is mediated by customer satisfaction

As shown before, several factors including ease of use, convenience, customisation, customer loyalty intention, satisfaction and word of mouth, which are the most important


with regard to this research, represent the key factors that influence the omnichannel customer experience. The proposed conceptual model of this study is presented in Figure 1 below:

Figure 1: Research model

Source: Adapted from Klaus and Maklan (2013)

3. Research Methodology

A descriptive research method was used to conduct the research survey. Respondents were asked to fill in the survey questionnaire using an online survey method on social media platforms. A structured questionnaire was prepared using the Google Docs form which was delivered to these initial respondents through their Facebook account. They were also asked to forward the survey web link to other potential respondents, using processes known as snowball sampling and convenience sampling. These sampling processes continued until the end of data collection. In the quantitative data collection, non-probability, convenience and snowball sampling techniques were followed to collect information. As this quantitative study had limited resources, these techniques were followed to select samples. Samples are easy to recruit using these techniques which are considered the easiest, cheapest and least time-consuming.

The items in our survey questionnaire were developed by adapting existing measures. The instruments for ease of use were adapted with some modification from a study conducted by McLean et al. (2018), as were the instruments for convenience and customisation. The instruments for customer satisfaction were collected from the study conducted by Klaus and Maklan (2013), as were the instruments for loyalty intention and word-of-mouth behaviour. All questions that included all the constructs’ instruments

Customisation

Convenience

Customer Experience (Outcome

Focus)

Ease of use

Word-of-mouth behaviour

Customer satisfaction

Loyalty intention


were designed with 5-point Likert scales, ranging from 1 to 5 with 1 denoting ‘strongly disagree’ and 5 as ‘strongly agree’.

The target population of this research comprised customers who had used several channels together, such as mobile apps and retail service or website and accessible retail service, to purchase products or services, with survey respondents being from Dhaka City. Dhaka was chosen as it is the capital city and the most densely populated city in Bangladesh. Moreover, access to advanced technology is higher in Dhaka City than in other cities; thus, it would be easier to find actual respondents there than in other cities. In the entire country, 23% of internet users shop online (Mahbub, 2016). In Dhaka, 86% of the population have access to the internet (BTRC, 2019), whereas 14% of Dhaka’s population are accustomed to making online purchases.

When population size is smaller in comparison to a large population, sample size is determined by following Cochran’s (1977) sample size formula, developed to calculate a proportional representative sample as follows:

where N is the population size (in our study, the population in Dhaka is 18,305,671 [Bangladesh Bureau of Statistics (BBS), 2018]); z is the selected critical value of the desired confidence level (95% confidence); and p is the estimated proportion (15%) of an attribute that is present in the population. Based on this formula, 195 respondents were considered to be enough to conduct the study. The pre-testing of the questionnaire was undertaken with a sample of 30 respondents before the final data collection. In our study, 450 questionnaires were distributed, with 230 suitable responses initially collected for analysis. After 27 questionnaires were omitted due to missing responses, in total, 203 questionnaires were selected for empirical analysis (a 45% response rate).

The conceptual model in Figure 1 was specified as the structural model with seven latent constructs: each construct was operationalised by a set of indicators or measurements included in the questionnaire. The structural model investigated moment structures, one of the most utilised statistical procedures that examines structural theory through a corroborative approach (Tabachnick et al., 2007). This structural model could be tested using partial least squares (PLS) as it has several advantages over other methods. Available as free distributed software, PLS can measure skewed distributions for indicators, misspecification and multicollinearity (Chin, 1998). In the current study, the estimation and tests of the model were conducted through SmartPLS (Ringle et al., 2005). All latent variables were measured by reflective indicators, with it assumed here that the indicators were a reflection of an underlying latent construct.


4. Findings and Analysis

4.1 Demographic Findings

Usable data were collected from 203 respondents. As shown in Table 1, the sample of customers in this study included more males (60.59%) than females (39.41%). Most omnichannel users (36.94%) were aged from 30–40. Regarding their professional status, 24.13% were entrepreneurs, 22% were businessmen, 16% were service holder, 12.80% were doctors, 12.13% were students and 11.82% were housewives. As for omnichannel usage status, 65% used two (2) channels, 28% used three (3) channels and 13% were four (4) channel users. As for frequency of transaction status, 26% were weekly users, 41% were monthly users, 12% were bi-monthly users, 14% used the omnichannel quarterly and 5.9% used the omnichannel annually.

Table 1: Respondents’ description (n=203)

Variables/

Dimensions

Frequencies Percentage Variables/

Dimensions

Frequencies Percentage

Gender Omnichannel Used

Male

Female

123

80

60.59

39.41

Used 2 channels

Used 3 channels

Used 4 channels

133

57

13

65.52

28.08

6.40

Age Transaction Status

20–30

30–40

40–50

50 and above

70

75

55

3

34.48

36.94

27.09

1.47

Weekly

Monthly

Bi-monthly

Quarterly

Annually

53

84

25

29

12

26.11

41.38

12.32

14.29

5.90

Monthly income Profession

Below 10,000

10,000–19,000

20,000–29,000

30,000–39,000

40,000 and above

19

20

49

55

60

9.35

9.85

24.13

27.09

29.55

Student

Entrepreneur

Businessman

Service holder

Housewife

Doctor

Engineer

25

49

45

34

24

26

18

12.31

24.13

22.16

16.74

11.82

12.80

8.88


4.2 Structural Equation Modelling (SEM)

The structural equation modelling (SEM) has been conducted in three stages: firstly, evaluating the measurement; secondly, testing the relationships in the structural model and providing the estimated model; and, thirdly, evaluating the final structural model. This sequencing helps to ensure the latent variables’ reliability and validity before estimating, hypotheses testing and drawing conclusions (Fornell & Larcker, 1981; Hulland, 1999).

4.3 Reliability and Validity Analysis

The first step in SEM is testing the measurement model which was conducted with CFA and using SmartPLS 3.0. The measurement model was first tested for reliability, convergent validity and discriminant validity before testing the hypotheses. Reliability was assessed by considering Cronbach's alpha coefficient values and composite reliability. In this study, 0.7 was the acceptable level for the factor loading. The recommended level for composite reliability was 0.7 and was 0.5 for average variance extracted (AVE) (Hair et al., 2016). Hair et al. (2013) have accepted a threshold of 0.7 for Cronbach’s alpha coefficient values.

Table 2: Convergent validity and internal reliability

Constructs Items Standard Loading

Cronbach’s Alpha

Composite Reliability

Average Variance Extracted (AVE)

Convenience Convenience 1 0.811 0.780 0.869 0.689

Convenience 2 0.833

Convenience 3 0.847

Customer Satisfaction

Customer Satisfaction 1 0.818 0.822 0.876 0.586

Customer Satisfaction 2 0.753




Customisation Customisation 1 0.773 0.702 0.834 0.627

Customisation 2 0.777

Customisation 3 0.824

Ease of use Ease of Use 1 0.749 0.827 0.872 0.537

Ease of Use 2 0.785

Ease of Use 3 0.733


Ease of Use 4 0.797

Ease of Use 5* 0.521

Ease of Use 6 0.776

Loyalty Intention

Loyalty Intention 1 0.819 0.806 0.866 0.565

Loyalty Intention 2 0.752

Loyalty Intention 3 0.804

Loyalty Intention 4* 0.689

Loyalty Intention 5* 0.684

Customer Experience (Outcome Focus)

Outcome Focus 1 0.745 0.731 0.832 0.553

Outcome Focus 2 0.806

Outcome Focus 3* 0.684

Outcome Focus 4 0.736

Word of Mouth Word of Mouth 1 0.797 0.858 0.898 0.639

Word of Mouth 2 0.728




Notes: *Indicates an item that was dropped in the scale purification process; all standardised coefficient loadings are significant at p < 0.01

It is suggested that the loadings of all standardised parameters be greater than 0.7 (Hair et al., 2013). As shown in Table 2, standard loadings ranging from 0.713–0.855 met the acceptable level of measurement. Items indicated by an asterisk (*) were removed in the scale purification process as they had an unacceptable level of measurement. Future researchers could pursue these through further investigation.

As shown in Table 2, Cronbach’s alpha coefficient values range from 0.702–0.858, while composite reliability (CR) ranges from 0.832–0.898; thus, both meet the acceptable level of measurement. Henseler et al. (2015) recommended that convergent validity could be studied satisfactorily if measurement constructs had an average variance extracted (AVE) value of at least 0.5. In the table, the AVE ranges from 0.537 to 0.689. Therefore, the conditions for convergent validity were satisfied.

4.4 Goodness of Fit

In this study, discriminant validity was evaluated by comparing the absolute value of the correlations between the constructs and the square roots of the AVE extracted by a construct. As shown in Table 3, all square roots of AVE values are higher than the


corresponding correlations between the constructs, with this result confirming that the study had adequate discriminant validity.

Table 3: Correlation matrix and square roots of average variance extracted (AVE) value

Convenience Customer Satisfaction

Customisation Ease of Use

Loyalty Intention

Customer Experience (Outcome

Focus)

WOM

Convenience 0.830


0.478 0.765

Customisation 0.559 0.478 0.792

Ease of Use 0.575 0.551 0.584 0.733

Loyalty Intention

0.446 0.737 0.505 0.497 0.752

Customer Experience

(Outcome Focus [OF])

0.313 0.631 0.460 0.426 0.628 0.744

Word of Mouth (WOM)

0.389 0.697 0.447 0.405 0.814 0.597 0.800

4.5 Hypotheses Testing

SmartPLS 3.0 statistical software was applied to investigate the structural path coefficients and the R-squared (R2) values of the endogenous variables used to evaluate the explanatory power of a structural model. The bootstrapping method was used to test the hypotheses at a level of significance of 0.05 (p<0.05). The hypotheses between dependent and independent variables were tested using the path coefficients (β) and t-statistics at a level of significance of 0.05 (p<0.05).

As shown in Table 4, customisation (t=3.971, β=0.322) and ease of use (t=3.137, β=0.241) had a significant effect on customer experience (outcome focus). Thus, Hypotheses 2 and 3 were accepted statistically. The study showed that convenience (t=0.072, β=-0.005, p=0.942) had no significant impact on customer behaviour (outcome focus). Hence, Hypothesis 1 was rejected. The study explored customer experience as the outcome focus (t=16.841, β=0.631); customer experience as the outcome focus (t=4.309,


β=0.271); and customer experience as the outcome focus (t=3.745, β=0.261) with all having a significant impact on customer satisfaction, loyalty intention and word of mouth. Thus, Hypotheses 4, 5 and 6 were accepted statistically. Customer satisfaction (t=9.497, β=0.566) and customer satisfaction (t=7.811, β=0.532) had a significant impact on loyalty intention and word of mouth and, thus, Hypotheses 7 and 8 were accepted statistically.

Table 4: Hypotheses testing

Hypothesis Relationship Std.

Beta

(β)

Standard

Deviation

(STDEV)

t-Statistics

(|O/STDEV|)

p-

Values

Decision

H1 Convenience

Outcome Focus

-0.005 0.070 0.072 0.942 Not

supported

H2 Customisation

Outcome Focus

0.322 0.081 3.971 0.000 Supported

H3 Ease of Use Outcome

Focus

0.241 0.077 3.137 0.002 Supported

H4 Outcome Focus


0.631 0.037 16.841 0.000 Supported

H5 Outcome Focus

Loyalty Intention

0.271 0.063 4.309 0.000 Supported

H6 Outcome Focus Word

of Mouth

0.261 0.070 3.745 0.000 Supported

H7 Customer Satisfaction

Loyalty Intention

0.566 0.060 9.497 0.000 Supported

H8 Customer Satisfaction

Word of Mouth

0.532 0.068 7.811 0.000 Supported

4.6 Mediation Effects

As shown in Table 5, the study examined all the direct and indirect effects of the mediation relationships for two variables: customer experience (outcome focus [OF]), and customer satisfaction (CUS SAT) in Table 5. In total, 17 mediation relationships (11 direct and six [6] indirect) were observed. In Hypothesis 9 (i, ii, and iii), the study found that customer experience (outcome focus [OF]) as a mediating variable established a significant relationship between the variables. The results showed that customer


experience (outcome focus [OF]) significantly mediated the relationship between: (i) ease of use and loyalty intention (t=2.570, β=0.065); (ii) ease of use and customer satisfaction (t=3.181, β=0.152); and (iii) ease of use and word of mouth (t=2.416, β=0.063). In this case, the results proved that Hypothesis 9 (i, ii and iii) was accepted. For Hypothesis 10 (i, ii, and iii), we observed a negative beta value and t-values were shown to have a very insignificant effect on the relationships. Thus, Hypothesis 10 (i, ii, and iii) was rejected. In Hypothesis 11 (i, ii, and iii), we found that customer experience (outcome focus [OF]) as a mediating variable had a significant effect on all the relationships. The results showed that customer experience (outcome focus [OF]) significantly mediated the relationships between (i) customisation and loyalty intention (t=2.935, β=0.087); (ii) customisation and customer satisfaction (t=3.693, β=0.203); and (iii) customisation and word of mouth (t=2.973, β=0.084).

In the case of indirect relationships, we observed six relationships. In Hypothesis 12 (i and ii), we observed that (i) the relationship between ease of use and customer loyalty mediated by customer experience (outcome focus) and customer satisfaction showed a significant effect (t=2.874, β=0.086). The relationship between ease of use and word of mouth mediated by customer experience (outcome focus) and customer satisfaction also showed a significant effect between them (t=2.874, β=0.081). The study showed an insignificant result for Hypothesis 13 (i and ii). The study showed that the relationship between convenience and customer loyalty mediated by customer experience (outcome focus) and customer satisfaction was insignificant and with a negative effect (t=0.075, β=-0.002), as was the relationship between convenience and word of mouth mediated by customer experience (outcome focus) and customer satisfaction (t=0.075, β=-0.002). For both these cases, we observed that the mediators had an insignificant effect. For Hypothesis 14 (i and ii), we found that the indirect effects were significant. We observed that the relationship between customisation and customer loyalty mediated by customer experience (outcome focus) and customer satisfaction had a significant effect (t=3.356, β=0.115). The relationship between customisation and word of mouth mediated by customer experience (outcome focus) and customer satisfaction also had a significant effect (t=3.182, β=0.108).

Lastly, in Hypothesis 15 (i and ii), we found the direct effect of customer satisfaction on the relationship of customer experience and loyalty intention. For this relationship, we found that customer satisfaction (CUS SAT) positively affected the relationship between the variables (t=7.388, β=0.357), and in the relationship between customer experience and word of mouth mediated by customer satisfaction (CUS SAT), we found a significant effect (t=6.801, β=0.336).


Table 5: Mediation effect

Hypothesis Mediation effect Std. Beta

(β)

Sample Mean (M)

Standard Deviation (STDEV)

t-statistics (|O/STDEV|)

p- Values

Decision

H9 (i) EU OF LOYALTY

0.065 0.067 0.025 2.570 0.010 Accepted

H9 (ii) EU OF CUS SAT

0.152 0.156 0.048 3.181 0.002 Accepted

H9 (iii) EU OF WOM 0.063 0.065 0.026 2.416 0.016 Accepted

H10 (i) CON OF LOYALTY

-0.001 0.002 0.018 0.075 0.940 Rejected

H10 (ii) CON OF CUS SAT

-0.003 0.005 0.042 0.077 0.939 Rejected

H10 (iii) CON OF WOM

-0.001 0.002 0.017 0.076 0.939 Rejected

H11 (i) CUSTOMISATION OF LOYALTY

0.087 0.085 0.030 2.935 0.003 Accepted

H11 (ii) CUSTOMISATION OF CUS SAT

0.203 0.198 0.055 3.693 0.000 Accepted

H11 (iii) CUSTOMISATION OF WOM

0.084 0.081 0.028 2.973 0.003 Accepted

H12 (i) EU OF CUS SAT LOYALTY

0.086 0.089 0.030 2.874 0.004 Accepted

H12 (ii) EU OF CUS SAT WOM

0.081 0.084 0.028 2.874 0.004 Accepted

H13 (i) CON OF CUS SAT LOYALTY

-0.002 0.003 0.024 0.075 0.940 Rejected

H13 (ii) CON OF CUS SAT WOM

-0.002 0.003 0.023 0.075 0.941 Rejected

H14 (i) CUSTOMISATION OF CUS SAT LOYALTY

0.115 0.113 0.034 3.356 0.001 Accepted

H14 (ii) CUSTOMISATION OF CUS SAT WOM

0.108 0.106 0.034 3.182 0.002 Accepted

H15 (i) OF CUS SAT LOYALTY

0.357 0.359 0.048 7.388 0.000 Accepted

H15 (ii) OF CUS SAT WOM

0.336 0.338 0.049 6.801 0.000 Accepted


Figure 2 below shows the path coefficients among the independent variables with the dependent variables and the coefficients of the relationships among the dependent variables. In the structural model, the R2 value represents the amount of variance explained by the structural model, as in multiple regressions. In the structural model, the R2 value for outcome focus (OF) is 0.249, indicating that the integrated value of the independent variables can explain 24.9% variance in outcome focus (OF). The value for R2 for loyalty is 0.587, indicating that the value of the outcome focus (OF) on the model can explain 58.7% variance in customer loyalty. The R2 value for customer satisfaction (CUS SAT) of 0.399 indicates that the value of outcome focus (OF) can explain 39.9% variance in customer satisfaction. The R2 value for word of mouth (WOM) of 0.527 indicates that the value of outcome focus (OF) on the model can explain 52.7% variance in customer word of mouth (WOM).

Figure 2: Conceptual model with results (p ≤ 0.05)


5. Discussion

Dealing with the customer experience is viewed today as one of the most unpredictable and pressured issues for administration associations worldwide (Badgett et al. 2007). Given the hypothetical contemplations, we inferred a conceptual model indicating the key factors that influenced the omnichannel customer experience outcome focus. The study analysed the fundamental values behind the omnichannel customer experience of respondents living in Dhaka, Bangladesh. Results indicate that the proposed model is an excellent predictive model for determining the factors affecting the omnichannel customer experience. The study has identified ideas related to customer experience and its significant positive influence on customer satisfaction, word of mouth and loyalty. The aim was to discover how the three distinct factors – ease of use, convenience and customisation – affected the omnichannel customer experience outcome focus (McLean et al., 2018), with this further affecting three other factors – loyalty intention, customer satisfaction and word of mouth. From the path coefficient analyses, CON, EOU, CUS SAT, loyalty and WOM were observed to have significant relationships with the omnichannel outcome focus, with these results consistent with the findings of prior research.

The results of the current study show that customisation positively impacts on customer experience outcome focus which matches the findings of Klaus and Maklan (2013), McLean et al. (2018) and Bilgihan et al. (2016). Surprisingly, the results for convenience were inconsistent with the customer experience outcome focus, with this not supported by McLean et al. (2018), McLean and Wilson (2016), Klaus and Maklan (2013) and Kim et al. (2009). These findings were inconsistent with the literature and were contradictory to conventional empirical and theoretical findings, thus emphasising the enormous importance of convenience on customer experience. Ease of use positively impacted on customer experience outcome focus, with this supported by Chau and Lai (2003), McLean et al. (2018) and Ha and Stoel (2008). Results therefore show that customisation, convenience and ease of use significantly and directly influence the omnichannel customer experience outcome focus (McLean et al., 2018).

Moreover, this study provides evidence of the actual nature of the omnichannel customer experience outcome focus and its noteworthy positive impact on loyalty intention, word of mouth and customer satisfaction. Outcome focus works as a mediating variable between ease of use, convenience, customisation and loyalty intention, word of mouth and customer satisfaction. Our findings show that outcome focus (customer experience) has an almost equal impact on all outcomes. Word of mouth is thought to play a vital role in many aspects of customer behaviour (Park et al., 2007). This study shows the strong positive influence of outcome focus (customer experience) on word of mouth, which is supported by Klaus and Maklan (2013).


The R-squared (R2) values demonstrate a noteworthy positive connection between customer satisfaction and loyalty intention, as satisfied customers are more likely to become loyal and repeat purchasers becoming key to firms’ profitability (Khadka & Maharjan, 2017). This examination demonstrates a noteworthy positive connection between customer satisfaction and loyalty intention, which is supported by the findings of Gronholdt et al. (2000) and Al-Wugayan and Pleshko (2011), but not by the findings of Andreassen and Lanseng (2010).

6. Contribution to Theory

This empirical study contributes theoretically to the omnichannel customer experience outcome focus in Bangladesh. The research proposes an integrated model of omnichannel outcome focus (customer experience) by considering multi-dimensional values from the customer perspective. It provides a more comprehensive framework than previous studies for understanding customer experience based on values. This empirical study contributes theoretically to the omnichannel field in Bangladesh as well as in other developing countries. This study shows the relationships between customer satisfaction, loyalty intention and word of mouth and the mediating role of outcome focus (customer experience) in this relationship. This integrated model is the first to be used in this field and has never previously been used in any study on the omnichannel around the world. This study provides a comprehensive framework that can be used in the future to also conduct more studies on other businesses or services sectors. The model helps to create a link between customer satisfaction, loyalty intention and word of mouth. It will assist future researchers to conduct a study on any aspects of this concept. The study shows the effects of ease of use, convenience and customisation on the outcome focus (customer experience) in the omnichannel field. The key determinants of customer experience outcome focus in this study have been used within the context of Bangladesh which will help researchers in other developing countries to use this theory in their prospective area.

7. Practical Implications

Apart from the theoretical implications, this study can be utilised by marketers and decision makers of businesses in Bangladesh as it reveals the variables that influence the omnichannel customer experience. Therefore, they can create increased amounts of customer satisfaction, loyalty intention and word of mouth. Businesspeople can use the findings to discover which instruments can be directed toward increasing the level of customer experience, and which instruments cannot. They can use the findings to compete on ease of use and convenience, as well as customisation. Finally, they will know how to attract customers through an increased level of business performance; they will also know how to retain existing customers. For example, the results show that convenience has less effect on customer experience, thus reflecting that this factor is not a ‘must do’ but if they can ensure it, it will help them to remain ahead in the competitive


business environment. Moreover, convenience and ease of use positively affect customer experience. The results also show that an increased level of customer satisfaction creates customer loyalty intention which, in turn, when combined with customer satisfaction, exerts a positive influence on word of mouth. Thus, entrepreneurs and marketers can potentially make good use of this study and its findings in the omnichannel business sector of Bangladesh.

8. Conclusion

This study was conducted to measure the factors that influence the omnichannel customer experience in Bangladesh and the relationships between ease of use, convenience, customisation and customer satisfaction, loyalty intention and word of mouth where the outcome focus has been assumed to play a mediating role in these relationships. The conceptual framework has been developed with exogenous and endogenous constructs that consider the context of Bangladesh. After analysis, it was found that the construct ‘convenience’ on the model had no positive impact on customer experience outcome focus, but all other dimensions seemed to have a statistically significant noteworthy impact on customer experience. When omni-shoppers utilise various mediums or touchpoints, they want a smooth and consistent experience while they are shopping. As reflected in the findings, convenience has no direct impact on the outcome focus in the omnichannel customer experience of Bangladesh; businesses should focus on convenience to gain a competitive edge. Furthermore, this may create customer satisfaction, loyalty and word of mouth. This study is a pioneer in investigating the variables influencing the omnichannel customer experience in Bangladesh. This study has affirmed that, in addition to agreement with past models, convenience, customisation, ease of use and customer satisfaction, loyalty and word of mouth are vital for the omnichannel customer experience in the Bangladesh context.

It is progressively harder to discover and draw in another customer than it is to retain an existing one (Schlesinger & Heskett, 1991). Therefore, improvement of the omnichannel customer experience in Bangladesh is expected to underline an expansion of the utilitarian variables of the omnichannel and customer satisfaction that will finally lead to an increased level of customer loyalty intention. Omnichannel businesses should increase all the factors that attract and retain customers’ patronage and loyalty. The study’s results show a positive connection between customer loyalty and word of mouth. This research has revealed an insight into the new omnichannel scenario as technology continues to change the face of retailing. This achievement will be guaranteed if every channel is coordinated flawlessly, considering buyers as customers and endeavouring to offer them an integrated and comprehensive shopping experience.

9. Limitations of the Study and Future Research Directions

Despite the theoretical and practical implications of the research, several limitations should be discussed to assist future researchers. Firstly, only a very few independent


variables were used to measure the customer experience. Other factors than those in the model as well as factors in other models could be used by other researchers from a different perspective or in different countries or different cultures to the measure the omnichannel customer experience. These factors could include timeliness, privacy, etc. Secondly, in this study, only outcome focus (customer experience) mediated the relationship between ease of use, convenience and customisation and customer satisfaction, loyalty intention and word of mouth. Some other constructs may be explored in future studies to mediate the relationships between these variables. Thirdly, this study includes the perceptions of respondents living in Dhaka City, whereas 64.96% of the Bangladeshi population live in the rural area. This may lead to cultural bias in the findings of the study and may not lead to generalisation of the study to Bangladesh as a whole.

Moreover, the theory used in this study can also be used in other businesses and services sectors. Entrepreneurs could also gain an insight into the scenario of the customer experience through this study. This study also presents the idea that people who browse yet do not buy online can be represented as a separate segment of potential online buyers. It might therefore be helpful to identify e-browsers’ shopping perceptions as these may differ from the perceptions of online buyers.

References

Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204-215.

Al-Wugayan, A., & Pleshko, L. P. (2011). Study of satisfaction, loyalty, and market share in Kuwait banks offering mutual fund services. Journal of International Business Research, 10(2), 1.

Anderson, E. W., & Mittal, V. (2000). Strengthening the satisfaction-profit chain. Journal of Service Research, 3(2), 107-120.

Andreassen, T. W., & Lanseng, E. J. (2010). Service differentiation: A self-image congruency perspective on brand building in the labor market. Journal of Service Management, 21(2), 212-236.

Arora, S., & Sahney, S. (2018). Antecedents to consumers’ showrooming behavior: An integrated TAM-TPB framework. Journal of Consumer Marketing, 35(4), 438-450.

Badgett, M. V., Lau, H., Sears, B., & Ho, D. (2007). Bias in the workplace: Consistent evidence of sexual orientation and gender identity discrimination. Chicago-Kent Law Review, 84, 559.

Bangladesh Bank. (2018). Bangladesh Bank Report 2018.

Bangladesh Bureau of Statistics (BBS) (2018).

Bangladesh Telecommunication Regulatory Commission (BTRC). (2018).


Berger, J., & Iyengar, R. (2013). Communication channels and word of mouth: How the medium shapes the message. Journal of Consumer Research, 40(3), 567-579.

Berry, L. L., Carbone, L. P., & Haeckel, S. H. (2002). Managing the total customer experience. MIT Sloan Management Review, 43(3), 85-89.

Bilgihan, A., Kandampully, J., & Zhang, T. (2016). Towards a unified customer experience in online shopping environments: Antecedents and outcomes. International Journal of Quality and Service Sciences, 8(1), 102-119.

Bontis, N., Bart, C., Wakefield, P., Booker, L. D., & Serenko, A. (2007). The mediating effect of organizational reputation on customer loyalty and service recommendation in the banking industry. Management Decision, 45(9), 1426-1445.

Burke, R. R. (2002). Technology and the customer interface: What consumers want in the physical and virtual store. Journal of the Academy of Marketing Science, 30(4), 411-432.

Caruana, A. (2002). Service loyalty: The effects of service quality and the mediating role of customer satisfaction. European Journal of Marketing, 36(7/8), 811-828.

Chau, P. Y., & Lai, V. S. (2003). An empirical investigation of the determinants of user acceptance of internet banking. Journal of Organizational Computing and Electronic Commerce, 13(2), 123-145.

Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295-336.

Chopra, S. (2016). How omni-channel can be the future of retailing. Decision, 43(2), 135-144.

Cochran, W. G. (1977). Sampling techniques (3rd ed.). New York, NY: John Wiley & Sons. Inc.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.

De Wulf, K., Odekerken-Schröder, G., & Van Kenhove, P. (2003). Investments in consumer relationships: A critical reassessment and model extension. The International Review of Retail, Distribution and Consumer Research, 13(3), 245-261.

Drabik, P., & Zamecnik, P. (2016). Key aspects of logistics for online store and multichannel distribution. In Proceedings of the 16th International Joint Conference: Central and Eastern Europe in the Changing Business Environment, May, Prague, Czech Republic (pp. 97-111).

Duncombe, R. A. (2014). Understanding the impact of mobile phones on livelihoods in developing countries. Development Policy Review, 32(5), 567-588.

Evans, C., Hackney, R., Rauniar, R., Rawski, G., Yang, J., & Johnson, B. (2014). Technology acceptance model (TAM) and social media usage: An empirical study on Facebook. Journal of Enterprise Information Management, 27(1), 6-30.

Fogg, B. J. (2002). Persuasive technology: Using computers to change what we think and do. Ubiquity, 5.


Fontanella, C. (2020). 15 examples of brands with brilliant omni-channel experiences. Retrieved from: https://blog.hubspot.com/service/omni-channel-experience (Accessed 13 February 2020).

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.

Fornell, C., Mithas, S., Morgeson III, F. V., & Krishnan, M. S. (2006). Customer satisfaction and stock prices: High returns, low risk. Journal of Marketing, 70(1), 3-14.

Gao, F., & Su, X. (2016). Omnichannel retail operations with buy-online-and-pick-up-in-store. Management Science, 63(8), 2478-2492.

Gefen, D. (2003). TAM or just plain habit: A look at experienced online shoppers. Journal of End User Computing, 15(3), 1-13.

Goldstein, S. M., Johnston, R., Duffy, J. & Rao, J. (2002) The service concept: The missing link in service design research? Journal of Operations Management, 20(2),121-134.

Gronholdt, L., Martensen, A., & Kristensen, K. (2000). The relationship between customer satisfaction and loyalty: Cross-industry differences. Total Quality Management, 11(4-6), 509-514.

Ha, S., & Stoel, L. (2009). Consumer e-shopping acceptance: Antecedents in a technology acceptance model. Journal of Business Research, 62(5), 565-571.

Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1-2), 1-12.

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135.

Herhausen, D., Binder, J., Schoegel, M., & Herrmann, A. (2015). Integrating bricks with clicks: Retailer-level and channel-level outcomes of online–offline channel integration. Journal of Retailing, 91(2), 309-325.

Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195-204.

Joy, A., Sherry Jr, J., Venkatesh, A., & Deschenes, J. (2009). Perceiving images and telling tales: A visual and verbal analysis of the meaning of the internet. Journal of Consumer Psychology, 19(3), 556-566

Kamel, J. P., & Kay, M. (2011). Opening the door to omni-channel retailing. Apparel Magazine, 53(2), 1-4.

Keiningham, T. L., Cooil, B., Aksoy, L., Andreassen, T. W., & Weiner, J. (2007). The value of different customer satisfaction and loyalty metrics in predicting customer retention, recommendation, and share-of-wallet. Managing Service Quality: An International Journal, 17(4), 361-384.


Khadka, K., & Maharjan, S. (2017). Customer satisfaction and customer loyalty. Centria University of Applied Sciences, Business Management.

Kim, C., Oh, E., Shin, N., & Chae, M. (2009). An empirical investigation of factors affecting ubiquitous computing use and U-business value. International Journal of Information Management, 29(6), 436-448.

Kim, S., & Jones, C. (2009). Online shopping and moderating role of offline brand trust. Direct Marketing: An International Journal, 3(4), 282-300.

Klaus, P., & Maklan, S. (2012). EXQ: A multiple scale for assessing service experience. Journal of Service Management, 23(1), 5-33.

Klaus, P. P., & Maklan, S. (2013). Towards a better measure of customer experience. International Journal of Market Research, 55(2), 227-246.

Kotler, P. (2000). Marketing management: The millennium edition. Marketing Management, 188-193.

Kotler, P., & Keller, K. L. (2006). Administração de marketing, 12.

Lazaris, C., Vrechopoulos, A., Katerina, F., & Doukidis, G. (2014). Exploring the “Omnichannel” shopper behaviour. In AMA SERVSIG, International Service Research Conference, June (pp. 13-15).

Mahbub, I. (2016). Only 23% of internet users in Bangladesh shop online. Retrieved from: https://futurestartup.com/2016/07/20/only-23-of-bangladeshi-internet-users-shop-online/ (Accessed 16 February 2020).

Mascarenhas, O. A., Kesavan, R., & Bernacchi, M. (2006). Lasting customer loyalty: A total customer experience approach. Journal of Consumer Marketing, 23(7), 397-405.

McLean, G., Al-Nabhani, K., & Wilson, A., (2018). Developing a mobile applications customer experience model (MACE) – Implications for retailers. Journal of Business Research, 85, 325-336.

McLean, G., & Wilson, A. (2016). Evolving the online customer experience … is there a role for online customer support? Computers in Human Behavior, 60, 602-610.

Melacini, M., Perotti, S., Rasini, M., & Tappia, E. (2018). E-fulfilment and distribution in omni-channel retailing: A systematic literature review. International Journal of Physical Distribution & Logistics Management, 48(4), 391-414.

Mena, C., Bourlakis, M., Hübner, A., Wollenburg, J., & Holzapfel, A. (2016). Retail logistics in the transition from multi-channel to omni-channel. International Journal of Physical Distribution & Logistics Management. 46 (6/7), 562-583.

Mowen, J. C., & Minor, M. (2001). Consumer behavior: A framework (2nd ed.). Upper Saddle River, NJ: Prentice-Hall.

Nasermoadeli, A., Ling, K. C., & Maghnati, F. (2013). Evaluating the impacts of customer experience on purchase intention. International Journal of Business and Management, 8(6), 128.


Park, D. H., Lee, J., & Han, I. (2007). The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement. International Journal of Electronic Commerce, 11(4), 125-148.

Picot-Coupey, K., Huré, E., & Piveteau, L. (2016). Channel design to enrich customers’ shopping experiences: Synchronizing clicks with bricks in an omni-channel perspective–the Direct Optic case. International Journal of Retail & Distribution Management, 44(3), 336-368.

Piotrowicz, W., & Cuthbertson, R. (2014). Introduction to the special issue information technology in retail: Toward omnichannel retailing. International Journal of Electronic Commerce, 18(4), 5-16.

Ringle, C. M., Wende, S., & Will, A. (2005), SmartPLS, Release 2.0., University of Hamburg, Hamburg, available at: www.smartpls.de

Rodríguez-Torrico, P., Cabezudo, R. S. J., & San-Martín, S. (2017). Tell me what they are like and I will tell you where they buy. An analysis of omnichannel consumer behavior. Computers in Human Behavior, 68, 465-471.

Rose, S., Hair, N., & Clark, M. (2011). Online customer experience: A review of the business-to-consumer online purchase context. International Journal of Management Reviews, 13(1), 24-39.

Schlesinger, L. A., & Heskett, J. L. (1991). The service-driven service company. Harvard Business Review, 69(5), 71-81.

Seiders, K., Voss, G. B., Grewal, D., & Godfrey, A. L. (2005). Do satisfied customers buy more? Examining moderating influences in a retailing context. Journal of Marketing, 69(4), 26-43.

Shankar, V., Smith, A. K., & Rangaswamy, A. (2003). Customer satisfaction and loyalty in online and offline environments. International Journal of Research in Marketing, 20(2), 153-175.

Shen, X. L., Li, Y. J., Sun, Y., & Wang, N. (2018). Channel integration quality, perceived fluency and omnichannel service usage: The moderating roles of internal and external usage experience. Decision Support Systems, 109, 61-73.

Sheth, J. N., & Mittal, B. (2004) Customer behavior: a managerial perspective. Thomson Learning, America.

Shobeiri, S., Mazaheri, E., & Laroche, M. (2015). How would the e-retailer’s website personality impact customers’ attitudes toward the site? Journal of Marketing Theory and Practice, 23(4), 388-401.

Silva, S. C. E., Martins, C. C., & Sousa, J. M. D. (2019). Omnichannel approach: Factors affecting consumer acceptance. Journal of Marketing Channels, 1-12.

Srivastava, M., & Kaul, D. (2014). Social interaction, convenience and customer satisfaction: The mediating effect of customer experience. Journal of Retailing and Consumer Services, 21(6), 1028-1037.

Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2007). Using multivariate statistics (Vol. 5). Boston, MA: Pearson.

Taylor, D. G., & Levin, M. (2014). Predicting mobile app usage for purchasing and information-sharing. International Journal of Retail & Distribution Management, 42(8), 759-774.


Toufaily, E., Ricard, L., & Perrien, J. (2013). Customer loyalty to a commercial website: Descriptive meta-analysis of the empirical literature and proposal of an integrative model. Journal of Business Research, 66(9), 1436-1447.

van der Wiele, T., Boselie, P., & Hesselink, M. (2002). Empirical evidence for the relationship between customer satisfaction and business performance. Managing Service Quality: An International Journal, 12(3), 184-193.

Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342-365.

Verhoef, P. C., Kannan, P. K., & Inman, J. J. (2015). From multi-channel retailing to omni-channel retailing: Introduction to the special issue on multi-channel retailing. Journal of Retailing, 91(2), 174-181.

Verhoef, P. C., Lemon, K. N., Parasuraman, A., Roggeveen, A., Tsiros, M., & Schlesinger, L. A. (2009). Customer experience creation: Determinants, dynamics and management strategies. Journal of Retailing, 85(1), 31-41.

Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology (UTAUT): A literature review. Journal of Enterprise Information Management, 28(3), 443-488

Wirtz, J., & Chew, P. (2002). The effects of incentives, deal proneness, satisfaction and tie strength on word-of-mouth behaviour. International Journal of Service Industry Management, 13(2), 141-162.

Yi, Y., & La, S. (2004). What influences the relationship between customer satisfaction and repurchase intention? Investigating the effects of adjusted expectations and customer loyalty. Psychology & Marketing, 21(5), 351-373.

Zhang, J., Farris, P. W., Irvin, J. W., Kushwaha, T., Steenburgh, T. J., & Weitz, B. A. (2010). Crafting integrated multichannel retailing strategies. Journal of Interactive Marketing, 24(2), 168-180.

Zins, A. H. (2001). Relative attitudes and commitment in customer loyalty models: Some experiences in the commercial airline industry. International Journal of Service Industry Management, 12(3), 269-294.

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