Academy of Strategic Management Journal (Print ISSN: 1544-1458; Online ISSN: 1939-6104)

Research Article: 2022 Vol: 21 Issue: 2

The Effect of Service Satisfaction on Use Motives and Channel Characteristics in Omni-Channel Based Pick-Up Service

Kyounghee Lee, ASSIST University

Boyoung Kim, ASSIST University

Citation Information: Lee, K., & Kim B. (2022). The effect of service satisfaction on use motives and channel characteristics in omnichannel based pick-up service. Academy of Strategic Management Journal, 21(1), 1-11.


As online shopping rapidly increases worldwide due to the popularization of the Internet, smartphones, and COVID-19, various omni-channel-based services are invigorated in the distribution industry. Significantly, the use of pickup service through offline stores has been recently growing, centering on young generations. This study aims to empirically present omni-channel characteristics and the effects of pickup service use motives in using omni-channel-based service when consumers are online shopping. It also assesses service satisfaction through the intervention of brand attachment and brand trust. A total of 324 questionnaire survey responses were collected and analyzed, focusing on the users' experiences of Korean online shopping omni-channel-based convenience store pickup service. According to the analysis results, personal motives affected both brand attachment and brand trust. Meanwhile, omni-channel characteristics affected brand trust but did not affect brand attachment. It was confirmed that brand trust, compared to brand attachment, further affected service satisfaction. Through all this, it needed to consider improving brand trust by taking into account individual consumers' use motives to enhance omni-channel-based pickup service satisfaction.


Omni-channel, Pick-up Service, Use Motives, Channel Characteristics, Service Satisfaction.


An age in which consumers can purchase desired goods without temporal and spatial restrictions has arrived due to Internet and smartphone diffusion worldwide. Consumers can buy goods using various channels, and corporate efforts to secure competitiveness are made using innovative technology adoption in the distribution market, as competition gradually becomes fierce. Since 2019, while contactless service has expanded due to COVID-19, Internet-based e-commerce has become an important distribution method.

Omni-channel service means a service by which consumers can search and buy goods through diverse channels, including online, offline, and mobile channels (Piotrwicz & Cuthbertzon, 2014). When looking at the priority of consumer requirements on omni-channel, price consistency is valued globally, but placing an order of goods and delivery status check are prioritized in Korea, followed by the delivery of goods that are not in stock in some stores and the return of goods ordered via online at offline stores (Yoo & Jung, 2002). Therefore, consumers think of delivery as necessary, along with placing an order on goods.

According to Harvard Business Review, the ratio of omni-channel customers using various channels such as on/offline was 73% after a survey of 46,000 shoppers in collaboration with companies having stores in the U.S. According to the stores’ survey results, omni-channel customers spent 13% more. The re-visiting ratio of the customers having omni-channel-based shopping experience in six months was 23%, and the possibility of recommending the brand they bought to their families and friends was higher than that of the customers using a single channel. As such, the distribution environment is expanded to Buy Online and Pick Up In-Store (BOPIS) delivery service by which consumers using omni-channel can place an order of goods online and pick up the ordered goods at their desired places. Best Buy ensures a competitive edge compared to Amazon by consolidating pickup service in stores, and Tesco is increasing delivery bases through an alliance with gas stations within the communities (Kim & Kim, 2016).

To prioritize omni-channel-based service, a customer’s purchasing experience should be elevated by offering BOPIS service (Bell et al., 2014). The BOPIS service has the advantage where consumers do not have to pay transportation costs and can quickly collect goods (Spanke, 2020). However, there were studies mainly on O2O service in previous studies concerned with omni-channel (Lim & Kim, 2018), studies on revenue change, performance, and intentions to purchase according to channel composition (Piotrowicz & Cuthbertson, 2014 Pauwels et al., 2011), and studies on interactions between consumers’ shopping channel movement factors and omni-channel operations (Bilgicer et al., 2015). As the omni-channel service is invigorated, a specific study on BOPIS service use from the consumer satisfaction and experience is needed.

This study aims to empirically present the effects of pickup service use motives in utilizing omni-channel-based service, when consumers are online shopping, and omni-channel characteristics on service satisfaction through the intervention of brand attachment and brand trust. This study presents effect factors on omni-channel-based pickup service and concrete implications for distribution companies’ marketing strategies.

Literature Review

Omni-channel Use Motives and Service Satisfaction

Consumers’ purchasing selection is driven by motives, perception, learning, and beliefs (Sichtmann, 2007). Motives coordinate acts by inducing consumers into markets (Dawson et al., 1990). Tauber (1972) saw that consumption motives include utility obtained through product purchase and demands not directly related to actual purchasing acts. Tauber classified consumption motives into personal motives and social motives. Personal motives refer to causes inducing consumption personally and include role-playing, diversion, and self-gratification, learning about new trends, physical activity, and sensory stimulation. Social motives are the causes induced by external conditions, not personal inner motives, including social experience, conversations with others, the pursuit of position and authority, and joy of price negotiation (Arnold & Reynolds, 2003). The consumption motives are one of the variables useful to understand consumer behavior.

Many studies on consumers’ consumption motives using the Internet shopping (Childers et al., 2001) divided Internet consumption motives into hedonic motives and practical movies and then classified them into multi-dimensions, such as economic, convenience, and social factors (Source et al., 2005). When looking at personal motives, the determinants of consumers’ purchasing act in the case of consumers pursuing practical motives are efficient functions related to product purchase, including price comparison by which products or services can be evaluated, a function reducing search time, and purchasing convenience (Seong et al., 2004). Hedonic motives based on emotional motives reflect preference regarding joy or happiness that consumers want to pursue through consumption (Dlodlo, 2014). Consumers have hedonic motives to purchase goods or services due to the emotional need for an exciting and joyful consumption experience.

Consumption motives encompass a satisfying consumption experience, including store facilities’ mood or mingling with others and functional and efficient values such as simply purchasing products or obtaining information (Hirshman & Holbrook, 1982). Because the desire that consumer may have by purchasing and consuming goods services are met beyond expectation, they can experience positive emotions on the transaction or about the transacting party (Lim & Dubinsky, 2004). Since individual consumer-pursuing use motives, customer satisfaction, and loyalty have close relations, motives become an essential factor determining the firm’s business performance in the online shopping market (Babin et al., 1994). Particularly, consumers' use motives before buying products affect consumption results (Schröder & Zaharia, 2008; Sung et al., 2004; Sorce et al., 2005).

Omni-channel Characteristics and Service Satisfaction

Omni-channel has an instant connectivity characteristic in which consumers can do shopping by searching necessary shopping information that they need through connecting with a network, regardless of time and place, via smart devices. Namely, instant connectivity provides technical convenience so that customers can have mobility in shopping, and it also grants temporal convenience (Huber et al., 2017). Localization refers to the degree of providing optimal shopping benefits to consumers in consideration of all information such as personal identification information, user location, and service connection duration (Gensler et al., 2012). This service means personalized advertisement is sent based on the customer’s location or shipping situation (Xu et al., 2011). The characteristics of instant connectivity and localization are said to positively affect the conversion of service consumers want to buy using omni-channel service in the shopping process (Chi et al., 2015;Hubert et al., 2017).

Customer support and customer recommendation system were mentioned as characteristics of channel interactivity, which provides prompt and instant customized service to consumers upon omni-channel use. Channel interactivity was explained as supporting customers to have a better shopping experience and make product search easier (Lim & Kim, 2018). Perceived convenience refers to the degree of perceiving inputted efforts as smaller when a new technology or system is used, compared to when the new technology or system is not used (Alrajawy et al., 2018). Therefore, as users perceive that a new technology and system may help them and perceive that the overall process can be easier, they regard the usefulness of the technology or system as higher, and their intention to use those increases as well.

The characteristics of omni-channel’s instant connectivity, localization, interactivity, and use convenience have a positive effect on consumer reactions, such as their perceived value, satisfaction, behavioral intention, and preference by offering more individually customized consistent experience (Beck & Rygl, 2015).

Omni-channel Service Brand Attachment and Trust

The omni-channel strategy offers a new empirical approach that can be differentiated from other brands strategically using on/off channels that the brand has while paying attention to consumers’ experience value (Kim & Kim, 2002). From this aspect, the brand attachment and trust that consumers feel in the omni-channel service can be crucial. Thomson et al. (2005) defined the strong emotion revealed in the long-term relations with the brand as a brand attachment. Verhagen & Dolen (2009) defined brand attachment as a trend forming emotional unity and bond and maintaining relations on the brand through repeated interactions with the specific brand.

From the continuous interaction process with specific brands that consumers purchase and use, the brand attachment, in which the emotional unity felt from themselves and the people close to them, was formed for the long-term, meaning that the emotional reactions they felt in their relations with people, namely love, interest, and affection, were experienced from a brand as well. Also, brand attachment is effectively used to form unity relations between brands and consumers (Fournier, 1998). Brand attachment positively affects brand loyalty or brand commitment that promotes consumer behavior, and the formation of relations between consumers and brands is carried out by brand attitude and brand attachment formation. A vital brand attachment role beyond a positive attitude on brand is emphasized (Chiou & Droge, 2006).

Brand trust is a customer’s belief in a brand, and it may be defined as one’s degree of believing that the brand performs its functions as promised (Erdem & Swait, 2004). As the degree of believing, whether on the expected quality or acts on consumers’ purchased products and services can be provided (Crosby et al., 1990), a synergy effect cannot be expected without trust on the existing channel in terms of consistent, integrated operation between various channels like omni-channel strategy (Terrling, 2007). Brand trust is an essential factor in customers’ selection and acquisition of products and services, and high-level trust is essential in forming relations (Mukherjee & Nath, 2007).

Omni-channel will have a more significant effect on relational quality with the brand concerned rather than another channel process in terms of experienced recognition and purchasing process in an integrated way, as various types of interactional communication is possible with consumers because of the omni-channel’s integrated structure (Verhoef et al., 2015) that is, brand attachment and brand trust on an omni-channel service need to consider relationship formation and maintenance between consumers and brands from the long-term perspective.

Research Method

Research Model and Hypothesis Development

As seen in the research model in Figure 1, this study empirically analyzes the effects of pickup service use motives in using omni-channel-based pickup service, when consumers are online shopping, and omni-channel characteristics on service satisfaction through the intervention of brand attachment and brand trust. To this end, this study composed practical, social, relational, and hedonic motives-based personal motives and the omni-channel characteristics including instant connectivity, localization, interactivity, and use convenience as independent variables.

Figure 1 Research Model

Based on the research model, the hypotheses were set using the following previous studies. Regarding decisions on product purchase or service use in specific consumption behavior, personal motives have a more significant effect than social motives (Mirsch et al., 2016). Sahney et al. (2014) asserted that personal motives such as economic, social and convenience affect consumers’ online shopping. Gensler et al. (2012) defined personal motives as four factors: practical, social, relational, and hedonic factors. The personal product purchase or service use motive factors affect mutual psychological aspects revealed in brand consumption behavior such as attachment and trust in specific brands (Lewis et al., 2014). This study presents the following hypotheses based on the previous studies of (Keng et al., 2007; Hirschman & Holbrook, 1982).

H1 Personal motives on omni-channel-based pickup service will have a positive effect on brand attachment.

H2 Personal motives on omni-channel-based pickup service will have a positive effect on brand trust.

As seen in the research model in Figure 1, this study empirically analyzes the effects of pickup service use motives in using omni-channel-based pickup service, when consumers are online shopping, and omni-channel characteristics on service satisfaction through the intervention of brand attachment and brand trust. To this end, this study composed practical, social, relational, and hedonic motives-based personal motives and the omni-channel characteristics including instant connectivity, localization, interactivity, and use convenience as independent variables. As the previous studies presented, Melero et al. (2016) asserted that consumers recognize and evaluate service based on temporal and physical convenience that they perceive while using omni-channel in the online or mobile environment. Therefore, omni-channel characteristics directly affect as effect factors judging consumption value in consumers’ product purchase and service selection or forming attachment or trust on service use in the online shopping process (Thomson et al., 2005; Teerling, 2007). This study presents the following hypotheses based on Schröder & Zaharia (2008) and Lim & Dubinsky (2004):

H3 Omni-channel characteristics on omni-channel-based pickup service upon online shopping will have a positive effect on brand attachment.

H4 Omni-channel characteristics on omni-channel-based pickup service upon online shopping will have a positive effect on brand trust.

Brand attachment is revealed as the degree of consumers’ cognitive and emotional bond and draws positive evaluation of products or services they use (Sung & Kim, 2010). Brand trust becomes a fundamental issue in constructing positive relations between companies and consumers and also draws consumer satisfaction with and loyalty towards brands (Salinas & Pérez, 2009). If brand attachment to and brand trust towards service are formed, consumers are satisfied with consumption behavior results and form loyalty and an intention of oral transmission (Srivastava, 2011). Therefore, this study presents the following hypotheses based on the previous studies of Ha (2004) and Japutra et al. (2014):

H5 Brand attachment to omni-channel-based pickup service upon online shopping will have a positive effect on service satisfaction.

H6 Brand trust on omni-channel-based pickup service upon online shopping will have a positive effect on service satisfaction.

Measurement Variable and Data Collection

This study composed questionnaire questions as shown in Table 1 through previous studies to analyze the presented hypotheses above. This study measured subjects’ experiences with a 5-point Likert scale on each survey item through 1 point for “Strongly disagree” and 5 points for “Strongly agree”.

Table 1 Variable Survey Items
Factors Survey Items References
Personal Motives (1) Pickup service is helpful for shopping.
(2) In pickup service, delivery service information is clear.
(3) Pickup service quickly conveys delivery service information.
(4) My acquaintances (family, friends, colleagues) are favorable to the pickup service.
(5) My acquaintances are highly interested in the pickup service.
(6) My acquaintances talk about the pickup service often.
(7) Pickup service reduces unnecessary communication upon goods delivery.
(8) I can communicate with new people through the pickup service.
(9) I post comments about the pickup service or engaged in online community activity.
(10) It is interesting using the pickup service.
(11) It is joyful to receive online shopping products using the pickup service.
(12) It is a very novel experience using the pickup service.
Keng et al. (2007)
Omni-channel Characteristics (1) Through the pickup service, purchased goods’ handling can be checked in real-time.
(2) For the pickup service, I can search and check the application information.
(3) I can immediately apply for the pickup service whenever I need the service.
(4) Pickup service identifies my location and provides information about the stores nearby.
(5) Pickup service provides useful shopping information in consideration of time or information that I want.
(6) Pickup service always offers accurate location information.
(7) I think the pickup service quickly responds to consumer needs.
(8) I can receive customized, one-on-one service if I use the pickup service.
(9) It is easy to call ARS or a consultant for the pickup service.
(10) It is convenient to apply for the pickup service.
(11) I conveniently receive goods through the pickup service.
(12) I can buy products that I want through the pickup service.
Schröder & Zaharia (2008); Lim & Dubinsky (2004)
Brand Attachment (1) I feel positive emotions towards the pickup service.
(2) I have an attachment to the pickup service.
(3) I have unity with the pickup service.
Ha (2004); Japutra et al. (2014)
Brand Trust (1) I have trust towards the pickup service.
(2) I think the pickup service offers a fair service to customers.
(3) I suppose an environment where consumers can safely do shopping is equipped with pickup service.
Chinomona (2016)
Service Satisfaction (1) I am satisfied with the pickup service overall.
(2) I am satisfied with selecting the pickup service as a delivery method.
(3) The pickup service was better than I expected.
Anderson & Fornell (2000)

Demographic Information of the Data

The questionnaire survey was carried out through an online survey targeting consumers in their 20s-50s having experience using the online shopping omni-channel-based convenience store pickup service. The survey was performed in a random sample collection method targeting all areas in Korea in May 2021. A total of 352 response copies were collected, and an analysis was performed with the 324 response copies except for insincere responses.

According to the composition ratio analysis result of the questionnaire survey participants, the ratio was 53.7% men and 46.3% women. The 20s, 30s, 40s, and 50s and over participants were 34.6%, 36.4%, 20.4%, and 8.6%, respectively. When looking at occupation groups, company employees took up the highest ratio at 60.84%, followed by students, professionals, self-employed, homemakers, and public officials at 11.4%, 11.4%, 3.4%, 4.3%, 5.4%, each. As for education, university graduates were highest at 78.2%, high school graduates were 7.7%, enrolled students in universities were 10.8%, and graduate school graduates were 13.3%.


Analysis Results of Reliability and Validity

Complex reliability was 0.827-0.930, and factor loadings were 0.600-0.815, so significance was secured. Cronbach α value was all 0.6 and over, so convergent validity was obtained (Table 2). Regarding the goodness-of-fit of the structural measurement model, Goodness-of-Fit-Index (GFI) value was 0.896, Adjusted Goodness-of-Fit-Index (AGFI) 0.830, Normal Fit Index (NFI) 0.886, and Root Mean Square Error of Approximation (RMSEA) 0.082, so component values were statistically significant.

Table 2 Results of Reliability and Convergent Validity Test
Classification Variable Standardized Loadings Standard Error t(p) CR Cronbach α
Personal Motives IM 1 0.731     0.882 0.867
IM 2 0.600 0.087 10.405***
IM 3 0.717 0.103 10.867***
IM 4 0.742 0.083 13.003***
Omni-channel Characteristics OC 1 0.763     0.930 0.889
OC 2 0.795 0.069 14.792***
OC 3 0.733 0.072 13.473***
OC 4 0.765 0.074 14.153***
Brand Attachment BA1 0.710     0.827 0.803
BA2 0.794 0.096 13.138***
BA3 0.788 0.095 13.057***
Brand Trust BT1 0.775     0.880 0.791
BT2 0.815 0.068 14.572***
BT3 0.782 0.071 14.005***
Service Satisfaction SS1 0.729     0.859 0.718
SS2 0.715 0.087 12.042***
SS3 0.758 0.090 12.739***

Note: Measurement model fit: χ² (p) 333.600(0.000), RMR 0.034, GFI 0.892, AGFI 0.830, NFI 0.896, TLI 0.905, CFI 0.926, RMSEA 0.082

The AVE, as shown in Table 3, was good between 0.616 and 0.768. As a result of analyzing each correlation coefficient, the correlation coefficients between variables were significant, so it was confirmed that discriminant validity was secured.

Table 3 Correlation Matrix and AVE
Variables AVE IM OC BA BT SS
Personal motives (IM) 0.652 0.818        
Omni-channel Characteristics (OC) 0.768 0.605 0.876      
Brand Attachment (BI) 0.616 0.416 0.328 0.787    
Brand Trust (BT) 0.710 0.350 0.421 0.362 0.821  
Service Satisfaction (SS) 0.669 0.411 0.484 0.245 0.398 0.836
The square root of AVE is shown in bold letters.

Analysis Results of Structural Model

As a result of analyzing the goodness-of-fit of the structural model, χ2(p) was 350.578(0.000), χ2 /degree of freedom 2.120, Goodness-of-Fit-Index (GFI) 0.881, Normal Fit Index (NFI) value 0.891, Comparative Fit Index (CFI) 0.921, Tucker Lewis Index (TLI) judging the structural model’s explanation power 0.899, Root Mean Square Residual (RMR) 0.0921, Adjusted Goodness-of-Fit-Index (AGFI) 0.830, and Root Mean Square Error of Approximation (RMSEA) 0.084. Thus, the component values of the goodness-of-fit were good overall, and the model’s goodness-of-fit was significant.

When looking at the hypotheses analysis result as shown in Table 4, personal motives had a positive effect on brand attachment (1.309, p<0.01) and brand trust (2.895, p<0.001). Personal motives affected brand trust more, compared to the brand attachment. The omni-channel characteristics positively affected brand trust (1.874, p<0.01) but did not affect brand attachment. Factors positively affecting service satisfaction were brand attachment (0.092, p<0.05) and brand trust (0.260, p<0.001), so the hypotheses were adopted. It was confirmed that brand trust affected service satisfaction more compared to the brand attachment. It was ascertained that brand trust could show an intervention effect that affects service satisfaction by personal motives and omni-channel characteristics. It was confirmed that brand attachment, meanwhile, can affect personal motives and service satisfaction but does not affect the omni-channel characteristics aspect.

Table 4 Results of Hypothesis Test
Hypothesis (Path) Standardized Loadings Standard Error t-value (p) Hypothesis Adoption
H1 Personal motives à Brand Attachment 6.571 5.018 1.309** Supported
H2 Personal motives à Brand Trust 2.926 1.011 2.895*** Supported
H3 Omni-channel Characteristics à Brand Attachment 5.566 4.892 1.138 Rejected
H4 Omni-channel Characteristics àBrand Trust 1.843 0.984 1.874** Supported
H5 Brand Attachment àService Satisfaction 5.665 61.59 0.092* Supported
H6 Brand Trust à Service Satisfaction 0.040 0.153 0.260*** Supported


This study analyzed the effect of pickup service use motives and omni-channel characteristics on service satisfaction through the intervention of brand attachment and brand trust among consumers using omni-channel-based pickup service upon online shopping. The implications drawn through the analysis are as follows:

First, personal motive factors affect brand attachment and brand trust. They affect brand trust more compared to brand attachment. This shows that brand trust from the promise and belief aspect works more than brand attachment from the emotional unity and bond aspect among consumers using omni-channel-based pickup service in terms of selecting and using the service. Consumers who use the omni-channel service hope to check product inventory, place orders conveniently, use online/offline coupons or discounts together, support various payment methods, and receive products quickly in their desired places. Consequently, it is implied that distribution companies trying to expand omni-channel-based pickup services should maximize trust towards and attachment to the offered service in consideration of consumers’ motives when they do Internet shopping.

Second, omni-channel characteristics affect brand trust, but not brand attachment. Regarding the omni-channel’s characteristics such as instant connectivity, localization, interactivity, and use convenience, the brand attachment aspect in using convenience store pickup service does not have tremendous meaning to the consumers. It is known that forming relations with consumers based on omni-channel characteristics affects brand trust, and service satisfaction can be obtained based on trust that brands promise to consumers using omni-channel-based pickup service.

Third, brand attachment and brand trust affect service satisfaction. It was confirmed that brand trust affects service satisfaction more compared to the brand attachment. Therefore, it was ascertained that the brand trust could exert an intervention effect affecting service satisfaction by the personal motives and omni-channel characteristics. Although brand attachment may affect personal motives and service satisfaction, it was confirmed that it did not affect the omni-channel characteristics aspect. The result shows that service satisfaction can be revealed further by brand trust in consumers’ using omni-channel-based convenience store pickup services. In the market environment where online shopping is common and omni-channel is expanded, high-quality products, the lowest prices, the best experience, and quick delivery should be provided. It should be noted that differentiation of omni-channel pickup service, meeting brand trust, and maintaining long-term relations based on trust can be more efficient.

By simultaneously examining omni-channel characteristics, which can be provided by personal motives that individual consumers have on the omni-channel-based pickup service, and the corporate aspect, this study obtains an academic meaning. The study empirically proved that those could be the factors for service satisfaction alongside brand attachment and brand trust. Nonetheless, the limitations of this study are as follows: First, omni-channel-based pickup service may have diverse channels such as department stores, large discount marts, and directly managed stores. However, this study researched convenience store users, so there may be a problem in generalizing to entire distribution companies. Second, this study targeted omni-channel-based pickup service users in online shopping malls, so a generalization limitation exists. If a study on group differences is carried out by comparing industrial groups where online shopping is expanded, drawing more meaningful implications from the practical work aspect is expected.


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