Examining customer channel selection intention in the omni-channel retail environment

Accepted Manuscript Examining customer channel selection intention in the omni-channel retail environment Xun Xu, Jonathan E. Jackson PII:

S0925-5273(18)30486-9

DOI:

https://doi.org/10.1016/j.ijpe.2018.12.009

Reference:

PROECO 7235

To appear in:

International Journal of Production Economics

Received Date: 16 March 2018 Revised Date:

1 December 2018

Accepted Date: 8 December 2018

Please cite this article as: Xu, X., Jackson, J.E., Examining customer channel selection intention in the omni-channel retail environment, International Journal of Production Economics (2019), doi: https:// doi.org/10.1016/j.ijpe.2018.12.009. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Examining Customer Channel Selection Intention in the Omni-Channel

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Retail Environment

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Xun Xu* Assistant Professor of Operations Management Department of Management, Operations, and Marketing College of Business Administration California State University, Stanislaus One University Circle, Turlock, CA, 95382, United States Email: [email protected] Telephone Number: 509-715-9076 Fax Number: 209-667-3210

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Jonathan E. Jackson Assistant Professor of Operations Management Department of Finance School of Business Providence College 1 Cunningham Square, Providence, RI 02918 Email: [email protected] Telephone Number: 401-865-2654

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* Corresponding Author. E-mail address: [email protected]

Submitted to International Journal of Production Economics for Publication Consideration

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Examining Customer Channel Selection Intention in the Omni-Channel Retail Environment Abstract

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Omni-channel supply chain management, with the objective of integrating multiple channels to achieve better overall performance across the entire supply chain, has been implemented by an increasing number of retailers. One of the challenges of omni-channel retailing and supply chain management is a result of

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the new and complex characteristics of the omni-channel retail environment; specifically, how to make customers more familiar with and adapt themselves to the omni-channel retail setting and to get them to

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utilize it to their advantage. Understanding customer perception is the first step in overcoming that challenge. This study examines the influential factors of customer channel selection intention in an omnichannel retail setting. Via an empirical analysis through surveys of customers (among whom a majority are online buyers) from the United States and United Kingdom, we find that channel transparency, channel convenience, and channel uniformity positively influence customer perceived behavioral control.

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In addition, we find that channel transparency and uniformity help reduce customers’ perceived risk, whereas channel convenience does not have a significant impact. Higher product price increases the influence of channel transparency, convenience, and uniformity on reducing customers’ perceived risk.

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Furthermore, we find customer perceived behavioral control and channel price advantage have a positive impact, and perceived risk has a negative impact on customer channel selection intention in the omni-

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channel retail environment. Our study provides an opportunity for omni-channel retailers to understand customer perception and needs, and better improve their offerings and supply chain management to attract more customer demand. Keywords:

Omni-channel retailing; omni-channel supply chain management; channel selection intention, channel transparency, channel convenience, channel uniformity

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1. INTRODUCTION Online sales in the United States have grown consistently in the retail sector since 2008. As of the fourth quarter in 2017, e-commerce sales make up just under 9% of total retail sales (U.S.

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Census Bureau News, 2018). The National Retail Federation and Business Insider both forecast the growth in online sales to continue, if not increase through 2020 (BI Intelligence, 2017). This continued growth in online sales has forced traditional brick-and-mortar retailers (e.g., Best Buy,

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Wal-Mart, Home Depot, Nordstrom’s) to adapt by introducing a secondary online channel. Online and offline demand are influenced by each other, which is a feature in the online to

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offline (O2O) era (Ji et al., 2017). The interactions between online and offline sales channels have been extensively studied in the operations literature (e.g., Choi et al., 2017; Zhang et al., 2017).

Today, retailers are blurring the traditional channel lines through the introduction of

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channels such as “buy online, pickup in store” (BOPS). Numerous retailers (e.g., Target, Lowe’s, Macy’s) have implemented a BOPS channel to provide consumers with maximum flexibility in their purchase options (in-store or online) and pickup options (in-store or home delivery)

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(Centric Digital, 2016). The introduction of BOPS and other alternative purchase channel offerings are leading retailers into omni-channel retailing. The literature on the development of

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omni-channel retailing and the impact of BOPS and other alternative purchase channel offerings are limited (e.g., Gao and Su, 2016a). Understanding how to integrate and coordinate these channels to better meet customer needs and demands are essential to enhance companies’ financial performance (Hübner et al., 2016). In response to omni-channel retailing, retail supply chains must adapt an omni-channel supply chain mentality. Specifically, the supply chain’s objective is to maximize performance

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across all channels in terms of the overall retail experience and total sales through the integration of all physical and digital channels (Verhoef et al., 2015). Additionally, omni-channel supply chains must be flexible and agile in their ability to make changes to the way in which orders are

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fulfilled to ensure efficiency and cost effectiveness (Ishfaq et al., 2016). These complex

interactions behind the scenes within a retail supply chain brings challenges to ensure that the customer is familiar and comfortable with the entire process through each unique channel

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offering (Verhoef et al., 2015). Few studies examine customer perception toward the omni-

channel supply chains (e.g., Bilgicer et al., 2015). Customers shopping at omni-channel retailers

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not only need to decide which product and retailer to choose, as in the traditional shopping environment, but also need to make one more decision: which channel to choose? This study aims to examine the influential factors on customer channel selection intention from three aspects: channel transparency, channel convenience, and channel uniformity. Channel

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transparency is from an information perspective, which refers to customers’ awareness of their updated order status in certain channel (Beldona et al., 2005). Channel convenience is from shopping experience perspective, which refers to customers’ perceived degree of avoidance of

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additional time and effort during the shopping process (Rohm and Swaminathan, 2004). Channel uniformity is from the service provider perspective, which refers to the consistency of the seller,

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fulfiller, and post-purchase service provider in an omni-channel supply chain (Martin et al., 2014). The information, shopping experience, and service provider factors reveal the complex features involved in the management of an omni-channel supply chain. Thus, by understanding customers’ perception toward these features can allow the retailer and their supply chain to better understand customers’ needs and make corresponding improvements to attract more customer demand. In addition, due to the complex features of an omni-channel supply chain, the role of

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customers’ perceived ease and control, namely, their perceived behavioral control, and their perceived risk in influencing their channel selection intention are worth examining. A better understanding of how product price and channel price advantage influence channel selection

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intention is also needed.

Our study has three primary research questions: 1) How do channel transparency, channel convenience, and channel uniformity influence customers’ perceived behavioral control and

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perceived risk?; 2) How does product price moderate the relationships between channel attributes and perceived risk?; 3) How do customers’ perceived behavioral control, perceived risk, and

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channel price advantage influence their channel selection intention? The main contributions of this study mainly lie on the fact that, to the best of our knowledge, this is one of the first studies examining the influential factors of customer channel selection intention in an omni-channel retail setting. Specifically, we seek to better understand how available information, the shopping

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experience, and consistency in the service provider through channel transparency, channel convenience, and channel uniformity within the omni-channel retail environment influence customers’ perceived behavioral control and risk, and ultimately their channel selection intention

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with the consideration of product price and channel price advantage. We use surveys distributed to omni-channel customers (among whom a majority are online buyers) to find the answers to

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those above research questions.

The rest of the paper is organized as follows. Section 2 reviews the relevant literature.

Section 3 introduces the theoretical background and hypotheses. The data analysis procedure is presented in Section 4. In Sections 5 and 6 the results are presented and then discussed. Section 7 discusses the theoretical and managerial implications. Finally, Section 8 concludes this study and proposes the future research directions.

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2. LITERATURE REVIEW 2.1 Omni-Supply Chain Management Since the turn of the century, more and more traditional brick-and-mortar retailers have taken

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their business online to leverage the customers who want the option of ordering on the internet. As such, the operations management literature has emphasized the challenges and opportunities facing firms introducing this second, or dual channel (e.g., Bendoly et al., 2007; Mahar et al.,

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2009). More recently, the retail market has continued its evolution into the realm of omni-

channel retailing, defined as “the set of activities in selling merchandise or services through all

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widespread channels, whereby the customer can trigger full channel interaction and/or the retailer controls full channel integration” (Beck and Rygl, 2015, p.174). Galipoglu et al. (2018) provided a useful overview of the omni-channel research to date through a content-analysisbased literature review. They noted the growing number of articles each year and the need for

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empirical studies with a strong theoretical foundation. The authors also categorized the omnichannel literature into four topics: multi-channel retailing, strategies, and customers’ channel behavior; strategic influence of the internet; analytical models on competition and channel

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conflict; and channel comparison.

Kumar and Hu (2015) forecasted omni-channel retail sales to eclipse $1.8 trillion in 2016

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and continue to grow up to $7 trillion by 2025. As such, retailers (and their supply chains) are working to innovate “a supply chain that increases product availability with flexible delivery options at a lower cost” (Kumar and Hu, 2015, p.76). In other words, omni-channel retailers require an omni-channel supply chain to ensure success in this new, and growing market. These new omni-channel supply chains amplify traditional challenges within supply chain management, including: developing a distribution network, optimizing fulfillment and distribution, integrating

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inventory availability information, and understanding customer perceptions and desires in an omni-channel retail setting. In an omni-channel supply chain, Levans (2014) discussed the widerange of distribution network configurations and purposes of distribution centers (DCs). The

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introduction of retail stores as fulfillment centers (Ishfaq et al., 2015) and traditional DCs being reconfigured to handle individual (online) orders and pallet (store) orders (Levans, 2014) has exemplified the determination of the optimal distribution network configuration to minimize

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fulfillment cost while maintaining a high service level. The integration of online and offline inventory availability information is a critical component to the success of an omni-channel retail

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operation and has been a major point of emphasis in the operations literature in the past five years (e.g., Bell et al., 2014; Gallino and Moreno, 2014; Gao and Su, 2016b). In addition to the distribution and inventory-based challenges facing omni-channel retailers, they must also pay attention to changes in customer perception with the increased

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flexibility and options provided to customers with regard to their purchases. Previous studies have found that customer characteristics and socio-demographics (Bilgicer et al., 2015) as well as their past-purchase behavior (Melis et al., 2015) influence their perception toward different

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purchasing channels. From the external aspect, companies’ marketing strategy (Melis et al., 2015) and channel management strategy (Ailawadi and Farris, 2017) influence customers’ perception

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toward different purchasing channels. Our study falls into this category of study that examines customer perception toward different purchasing channels from a variety of external aspects, but in an omni-channel retail setting. Specifically, we examined three characteristics of the omnichannel retail setting: transparency, convenience, and uniformity on customer purchase intention. By understanding the key characteristics that impact a customer’s choice of channel within an

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omni-channel retailing setting, retailers’ can then better structure their omni-channel supply chains to meet the needs and expectations of their customers. 2.2 Customer Purchase Intention and Channel Selection Intention

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Customer purchase intention has been widely studied in both offline (e.g., Creyer, 1997; Young et al., 1998) and online contexts (e.g., Fang et al., 2014; Wang and Hazen, 2016). Studies

throughout the years have identified a range of factors that influence customer purchase intention.

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Historically, customer purchase intention research has flourished in the marketing literature, including Brown et al. (2003) who found that product type and prior purchase experience

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influenced online purchase intention and Chang and Chen (2008) who found that online environmental cues (e.g., website quality and brand) also influence purchase intention. More recently customer purchase intention has been incorporated into the operations and supply chain management literature as well. Rao et al. (2011) explored the impact of operations

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glitches, primarily due to order fulfillment delays, on future purchase intentions. Wang and Hazen (2016) as well as Khor and Hazen (2017) focused on customer purchase intention of remanufactured products through the impact of subjective norms, risk, and perceived value and

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behavioral control. Additionally, Wan et al. (2016) analyzed the impact of online procurement and fulfillment processes on customer repurchase intention. This study aims to build off the prior

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operations and supply chain management literature related to purchase intention through the incorporation of an omni-channel retail setting. In an omni-channel retail setting, companies are not only worried about customer

purchase intention, but also the channel they plan to leverage to make their purchase. Ansari et al. (2008) discussed customers’ channel choice and found that marketing communications (e.g., catalogs and emails) influence customers’ purchase frequency and order size and affect their

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channel selection behavior. Their findings are supported by Neslin and Shankar (2009), who claimed firms can use marketing communications to guide customer channel choice. Konuş et al. (2008) found shopping enjoyment, loyalty, and innovativeness can predict if a customer is a

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multichannel enthusiast, an uninvolved shopper, or a store-focused consumer. Each market

segmentation has unique preferences on channel selection. Verhoef et al. (2007) focused on research shopping, which describes customers search and purchase behavior from different

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channels (e.g., searches at stores and then buy from e-retailer’s website). They found omnichannel retailers integrate the internet and store to meet customers’ searching and purchasing

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needs. Different from the above studies, this study examines customers’ channel selection intention in an omni-channel retail environment through analyzing the impact of channel attributes (transparency, convenience, and uniformity) on channel selection intention by influencing customer perception (perceived behavioral control and perceived risk).

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3. THEORETICAL BACKGROUND AND HYPOTHESES DEVELOPMENT The theoretical background of this study relies on two well-referenced theories: the theory of planned behavior (TPB) and the commitment-trust theory (CTT). TPB is an extension off the

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theory of reasoned action (Sheppard et al., 1988) through the addition of perceived behavioral control (Ajzen, 2002). TPB links humans’ beliefs and behavior and emphasizes the role of an

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individual’s perception on their intentions and behaviors (Ajzen, 2002). Perceived behavioral control in this study refers to the perceived ease or difficulty of performing the behavior (Ajzen, 2002), namely, channel selection for a purchase. More recently, Khor and Hazen (2017) used TPB when analyzing customer perceptions on remanufactured goods. Trust describes the belief that the human behavior will follow expectation (Luhmann, 1979). CTT describes how customers’ trust toward sellers can motivate them to build a long-

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term relationship with sellers, which reduces their perceived risk of shopping transactions, and increases their purchase intention (Mukherjee and Nath, 2007). Perceived risk in this study refers to customers’ perception of uncertainty about the advantages and disadvantages on shopping in a

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particular channel (Peter and Ryan, 1976). In this study, we examine the influence of channel transparency, convenience, and uniformity on customers’ perceived behavioral control and

perceived risk; the moderating role of product price; and the influence of perceived behavioral

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control, perceived risk, and channel price advantage on customers’ channel selection intention. 3.1 Channel Transparency

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Customers have a high desire for transparency during the shopping process in both the traditional brick-and-mortar setting (Awad and Krishnan, 2006) and in the online setting (Nguyen et al., 2016). In an omni-channel setting, channel transparency can come in many forms, including inventory visibility across channels (Zhang et al., 2011) and the ability to track orders throughout

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the process from purchase to delivery (Thirumalai and Sinha, 2005; Otim and Grover, 2006). This study will emphasize the latter. Transparent processes and efforts made by vendors can enhance customers’ familiarity of the channel (Grabner-Kraeuter, 2002) and transparent

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information can facilitate customers to evaluate the shopping process and compare the vendor performance (Beldona et al., 2005), both of which enhance customers’ perceived behavioral

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control. Based on the above discussion, this study proposes the following hypothesis: H1a: Higher channel transparency has a positive impact on customers’ perceived behavioral control.

One of the sources of online transaction risk comes from the lack of transparency of legal

norms in online markets compared with brick-and-mortar shopping transactions (GrabnerKraeuter, 2002). Transparency can enhance the procedural justice of the transaction during the

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shopping process and increase customer confidence with the shopping environment (Martin et al., 2011), both of which enhance customers’ trust (Kuo and Wu, 2012). Providing transparency can motivate customers and vendors to have more interactions with each other, and thus reduce the

Therefore, the following hypothesis is proposed:

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information asymmetry effects, which reduces the perceived risk (Lynch Jr. and Ariely, 2000).

H1b: Higher channel transparency has a negative impact on customers’ perceived risks.

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3.2 Channel Convenience

Higher channel convenience stimulates customers’ positive perception toward the channel due to

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both monetary and non-monetary (e.g., time and effort) savings (Berry et al., 2002). Boyer and Hult (2006) found similar customer attitudes in the grocery home delivery service; specifically, a significant preference for channels with high degrees of convenience (time savings). This phenomenon is only exacerbated in an omni-channel retail environment. Results from Murfield

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et al. (2017) suggest that omni-channel shoppers are primarily driven by convenience (time savings). Ma (2017) also explored the connection between customer satisfaction and logistics service quality (delivery time) in an omni-channel setting while also including the impact of free

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shipping. More generally, higher convenience allows customers easier access to a particular channel to search and ultimately purchase a product, which enhances customers’ perceived

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behavioral control (Seiders et al., 2000). Customers are more likely to revisit a convenient channel, which in turn increases their knowledge, familiarity, and loyalty to that channel, which enhances their perceived behavioral control (Jiang et al., 2013). Based on the above discussion, this study proposes the following hypothesis: H2a: Higher channel convenience has a positive impact on customers’ perceived behavioral control.

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Our study focuses on channel convenience from two perspectives: ease of searching and information gathering and shopping flexibility (time and location). Searching and information gathering in a retail setting can add value to the shopping experience (Jiang et al., 2013), but

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requires both time and effort (Vermeir and Van Kenhove, 2005). Retailers must not only ensure that information is available (channel transparency), but it is also easy to access (channel

convenience) (Rohm and Swaminathan, 2004). When comparing channel transparency and

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convenience, channel transparency tends to be more objective, whereas channel convenience is more subjective. Channel convenience affects the perception of comfort and ease of use (Jiang et

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al., 2013). In this way, channel transparency describes the results of accessing the information, and channel convenience describes the process of accessing the information. Customers’ information searching behavior depends on their comparison between perceived usefulness (i.e., information value) and perceived costs (i.e., time and effort expended)

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(Chung and Tan, 2004). Higher channel convenience hinges on minimizing time and effort expended (Seiders et al., 2007). Previous studies (e.g., Forsythe and Shi, 2003; Hong, 2015) have identified time loss as one of six types of perceived risk. Therefore, we expect a more convenient

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channel to minimize the time and effort requirements of the searching and purchasing process, which in turn will reduce customers’ perceived risk. This is particularly relevant for customers

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who have a high convenience orientation (Kollman et al., 2012). Channel convenience also reduces another type of perceived risk: psychological risk

(Forsythe and Shi, 2003). The second component of channel convenience is the flexibility it provides in terms of shopping location and time of day. This reduces fear and frustration with the shopping process and subsequently reduces perceived risk (Collier and Sherrell, 2010). It enhances customers’ perceived ease and comfort with the purchase channel, which induces them

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to feel that there is a sufficient fit between their purchase channel and their self-image, and thus reduces their perceived risk (Hong, 2015). Based on the preceding discussion, we propose the following hypothesis:

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H2b: Higher channel convenience has a negative impact on customers’ perceived risks. 3.3 Channel Uniformity

The growth of third-party sellers through companies like Wal-Mart create a discontinuity within

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a purchase channel. We use the term “channel uniformity” to analyze how consistency in the seller and fulfiller of an online order impacts a customer’s perceived behavioral control and

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perceived risks of using that channel. When a channel has channel uniformity (i.e., the seller and fulfiller are the same company), it increases the comfort level of the customer using that channel through enhanced perception of privacy and security, which in turn increases their perceived behavioral control (Kim et al., 2008). Channel uniformity also enhances customers’ perception

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on service quality consistency throughout each stage of the shopping process. Knowing that the same company will provide customer service throughout the process from purchase to delivery results in an increase in customers’ perceived behavioral control (Verhagen and Van Dolen,

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2009). Therefore, this study proposes the following hypothesis: H3a: Channel uniformity has a positive impact on customers’ perceived behavioral control.

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Channel uniformity, and the consistency that it provides, facilitates customers to build

long-term relationships with those vendors, which in turn reduces perceived risks of that channel (Moeller et al., 2009). Perceived risks are also reduced through the consistency of information delivery that can only be provided by a vendor who is the seller and fulfiller (Kim et al., 2008). Chang and Chen (2008) also emphasized the increase in trust through channel uniformity, which

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also reduces the perceived risk of using a particular channel. From this discussion, we propose the following hypothesis:

3.4 The Moderating Role of Product Price

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H3b: Channel uniformity has a negative impact on customers’ perceived risks.

Product price is an important determinant of customer perception and is related to the perceived value of the products (Iglesias and Guillén, 2004) and can moderate the relationship between the

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attributes of a channel and customers’ perception and behavior (Ryu and Han, 2010). Higher product price can also impact customers’ perception about shopping risk (Bettman, 1973b).

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When a customer buys an expensive product, the customer endures more financial risks. This triggers a desire to receive more information updates throughout the purchasing process to alleviate information asymmetry (Lee, 2008; Tsai et al., 2011). The higher price also enhances customers’ quality expectations, and thus motivates them to look for more about how the

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products are ordered, handled, and fulfilled through the distribution channel (Anderson et al, 1994). Customers are also more likely to experience psychological perceived risk when waiting for an expensive order to be delivered (Cameron et al., 2003). This thus makes them value the

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channel transparency more. Higher channel transparency can enhance customers’ perception of price fairness, especially when the price is higher, due to the understanding about more details of

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the pricing, and thus reduces customers’ perceived risks (Bolton et al., 2003). Based on the above discussion, we hypothesize: H4a: Higher product price increases the influence of channel transparency on reducing customers’ perceived risk.

When the product price is higher, customers have a higher psychological need to invest more time and effort into the information gathering step of the purchasing process (Chen and

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Dubinsky, 2003; Kim et al., 2002). However, an increase in information searching can result in information overloading issues (Jacoby, 1984). For high priced products, the role of channel convenience in influencing customers’ perceived risk becomes more important due to the

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increase in time investment in customers searching and information gathering (Agarwal and Teas, 2001). Higher channel convenience can make customers reduce the perceived sacrifice due to the

2001). Therefore, we propose the following hypothesis:

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higher financial costs, and thus significantly reduce their perceived risk (Agarwal and Teas,

H4b: Higher product price increases the influence of channel convenience on reducing

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customers’ perceived risk.

Purchasing a high-priced product makes customers more cautious about the transaction and motivates them to increase the number of interactions with vendors about the product order and fulfillment process (Ba and Pavlou, 2002). Thus, higher channel uniformity reduces

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customers’ social risk through the development of trust and long-term relationship with the same vendors (Revilla and Knoppen, 2015). Customers paying a higher price expect more utilitarian and hedonic value from the shopping process, and thus are more eager to get the products when

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ordered and more frustrated waiting during the fulfillment process (Chiu et al., 2014). A higher price also increases customers’ concerns about transaction privacy and security, and thus

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customers value using the same seller and fulfiller to reduce the perceived risk (Nepomuceno et al., 2014). These all emphasize the need for channel uniformity in reducing customers’ perceived risk when the product price is high. Based on the proceeding discussion, the following hypothesis is proposed:

H4c: Higher product price increases the influence of channel uniformity on reducing customers’ perceived risk.

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3.5 The Impact of Perceived Behavioral Control, Perceived Risk, and Channel Price Advantage on Channel Selection Intention According to TPB, perceived behavioral control influences customers’ intention and behavior

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because customers use perceived behavioral control to judge the likelihood of successfully performing the behavior or accomplishing the task (Kidwell and Jewell, 2003). Perceived

behavioral control is broken into two components: perceived control and perceived difficulty

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(Chen, 2007). Perceived control can increase as a result from a convenience channel to use, a channel which the customer is comfortable with or has experience using, or a channel which

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provides clear and transparent information about the process from purchase to delivery. If a channel provides a customer higher perceived control, then their purchase intention also increases (Chen, 2007). On the other hand, the perceived difficulty of using a channel can result from obstacles and challenges in searching for products or interacting with the vendor, which can

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easily prevent the customer from making a purchase (Hansen et al., 2004). Based on the preceding discussion, we propose the following hypothesis: H5: Higher perceived behavioral control has a positive impact on customers’ channel selection

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intention.

Customers who perceive additional risks associated with a particular channel in an omni-

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channel setting will decrease their likelihood of making a purchase in that channel. These additional risks could be connected to concerns over transaction security (Chang and Chen, 2008) or general uncertainty about the processes involved in transactions in a particular channel (Howard and Sheth, 1969). An increase in perceived risk brings doubt into the necessity of purchases (Park et al., 2005). This can be especially true in online-based channels where it is difficult to assess the quality and utility of products, and difficult to know the ease of which the

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transaction will occur (Kim and Lennon, 2013). Therefore, this study proposes the following hypothesis: H6: Higher perceived risk has a negative impact on customers’ channel selection intention.

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Customers can be price-sensitive (Abad and Jaggi, 2003) and retailers can have different pricing across their various channels (Zhang et al., 2017). According to utility theory (Thaler, 1985), customers make a transaction by comparing the benefits and costs. A lower cost can

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positively increase the gap between benefits and costs, which enhances customers’ purchase intention (Lichtenstein et al., 1990). Customers often search and compare prices among products,

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vendors, and channels, and price is a critical factor for them to make the purchase decision (Abad and Jaggi, 2003). The predominance of information technology in omni-channel retailing facilitates customers’ price comparisons (Verhoef et al., 2015). Customers can accept price differences between various channels if they believe the operational costs between the channels

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are different but can become frustrated with inconsistent pricing across channels (Parker Avery Group, 2018) and tend to utilize the channel price advantage to purchase from the cheaper channel (Fassnacht and Unterhuber, 2016). Customers can sacrifice and tolerate other aspects of

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products and services such as lower quality and limited service when the price is lower (Hellofs and Jacobson, 1999). Therefore, we hypothesize:

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H7: Channel price advantage (i.e., lower channel price) has a positive impact on customers’ channel selection intention.

The conceptual model of the hypotheses in this study are presented in Figure 1.

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Figure 1: Conceptual model.

H1a (+) Perceived Behavioral Control

H1b (-)

H2a (+) H2b (-)

Perceived Risk

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H3a (+)

Channel Uniformity

H5 (+)

H6 (-)

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Channel Convenience

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Channel Transparency

Channel Selection Intention

H7 (+)

Channel Price Advantage

H3b (-)

H4c (-)

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H4a (-) H4b (-)

Product Price

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4. DATA ANALYSIS

4.1 Data Collection and Sample

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A two-step procedure was used to develop the survey instrument. First, based on previous studies about channel characteristics (e.g., Bettman, 1973a; Jiang et al., 2013; Martin et al. 2014; Fassnacht and Unterhuber, 2016; Otim and Grover, 2016) and customer perception (e.g., Ajzen, 2002; Chang and Chen, 2008; Wang and Hazen, 2016), we developed the survey instruments revealing each latent variable in this study (see Table 2 for more details). The finalized survey included eight sections of questions associated with the corresponding eight latent variables in this study. 17

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The first five sections focused on channel characteristics; specifically, channel transparency, channel convenience, channel uniformity, product price, and channel price advantage. Each channel characteristic was measured by several items adopted from previous

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studies. Items that were used to measure channel transparency include questions about the

transparency of the transaction and delivery information including the time of the order being received, shipped, and delivered (Otim and Grover, 2016). For channel convenience, the

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questions focused on the ease of searching and information gathering as well as the time and location convenience of omni-channel shopping. (Jiang et al., 2013). For channel uniformity, the

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questions related to the seller’s responsibility in the entire transaction process, which include ordering, shipping, tracking, and post-sales services (Martin et al., 2014). The fourth channel characteristic focused on product price. The items for this characteristic varied between the U.S. and U.K. surveys. In the U.S. survey, product price is measured by the relative purchase price of

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the product compared with other products and customers’ willingness to pay a premium (Bettman, 1973a). Meanwhile, in the U.K. survey, product price is measured by the actual product price (in GBP). Similar to product price, the items for channel price advantage vary

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between the U.S. and U.K. surveys. In U.S. survey, channel price advantage is measured by the perception of the channel’s marked price, shipping and handling cost, and total price of the

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product compared with other channels (Fassnacht and Unterhuber, 2016). Meanwhile, in U.K. survey, channel price advantage is measured by the price gap elaborated in Section 4.2. The next three sections of our survey emphasized customer perception; specifically,

perceived behavioral control, perceived risk, and channel selection intention. The construct of perceived behavior control included questions about the internal control a customer has when using this channel as well as any potential external influences that may prevent them from using

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this particular channel (Ajzen, 2002; Kang et al., 2006). The construct of perceived risk focused on customers’ worries about the channel’s performance, ability to meet the stated delivery date, and customer service (Wang and Hazen, 2016). Finally, the last section focused on channel

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selection intention. These questions were about the customers’ willingness and intention to

choose or encourage other people to choose this channel for future purchases (Chang and Chen, 2008).

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After developing the survey instruments by referring to previous literature, we then

conducted a pilot test of the survey among college students from two universities, one in the

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Southwestern United States and the other in the Northeastern United States. A total of 132 questionnaires were collected to conduct an exploratory factor analysis (EFA) using the Varimax rotation method. From the results of the EFA, we excluded any items with low item-to-total correlation values. We also excluded any items that loaded on more than one construct to achieve

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unidimensionality. At this point the three-component survey instrument was finalized: (1) prequestions to establish the eligibility of respondents regarding their recent product shopping/searching experiences (e.g., had they bought a product from an omni-channel retailer);

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(2) multiple questions on the participant’s perception corresponding to each of the eight latent variables using a five-point Likert scale (i.e., strongly disagree, disagree, neither disagree nor

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agree, agree, and strongly agree); (3) demographic information questions about the participants. The survey is available as an online appendix (as can be found in Appendix A) or upon request to the first author.

Second, after the survey instrument was finalized, we collected data from participants on

Amazon’s Mechanical Turk (MTurk) who had recently made purchases where multiple purchasing channels were available. To eliminate cross-national effects, referring previous

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studies (e.g., Berinsky et al., 2012; Eriksson and Simpson, 2010; Huff and Tingley, 2015; Lee et al., 2018; Xu and Gursoy, 2015), we restricted the IP address to only allow respondents in the United States. These cross-national effects can be a result of cultural differences (Kim et al.,

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2002) or varying shopping environments (Baker et al., 2002), which can influence customers’ perceptions and behaviors during the shopping process. In Section 4.2 we test our model in the United Kingdom for consistency in the findings with the hope of generalizing our results. The

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incentive for the participants was 1 USD upon meeting three criteria: (1) meeting pre-question eligibility, (2) passing all validation check questions throughout the survey, and (3) completing

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all questions in the survey. Our validation check questions follow previous studies (e.g., Presser et al., 2004) by randomly inserting validation check questions throughout the survey. As an example, one validation question may say “Please choose ‘agree’ for this question.” If a participant failed a validation check question, the survey immediately sent them to a conclusion

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screen and the participant was not compensated. These questions minimize the likelihood that a participant is not reading the questions and answering randomly. The participant’s reward was paid within three days after one of the authors confirmed that the participant met each of the

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three criteria. A total of 815 respondents started the survey. After filtering on our three criteria, a total of 507 valid responses remained and were used for statistical analysis.

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4.2 United Kingdom Customers: An Extension We collected a second sample from omni-channel customers in the United Kingdom to test for consistency in our model with the hope of generalizing our findings. To extend the model and test for robustness, in the United Kingdom (U.K.) model, referring to previous studies (e.g., Ailawadi and Farris, 2017; Verhoef et al., 2015), we modified our measure for product price and channel price advantage to be more objective in comparison with the original U.S. model. More

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specifically, the product price is the true price paid (in GBP). The channel price advantage is measured by the price gap between channels. In detail, in the survey, customers provide the total price (product price plus any additional shipping or handing costs) for each available channel. If

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the channel the customer chooses is the cheapest channel, then the channel price advantage is calculated by the gap between that channel and the second cheapest channel, which results in a positive number. If the channel the customer chooses is not the cheapest channel, then the

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channel price advantage is calculated by the gap between that channel and the cheapest channel, which results in a negative number. The remainder of the U.K. survey is identical to the original

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U.S. survey (including the rejection criteria and compensation). A total of 379 U.K. respondents started the survey. After filtering on our three criteria, a total of 260 valid responses remained and were used for statistical analysis. 4.3 Profile of Respondents

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Table 1 shows the demographic characteristics (i.e., gender and age) of the respondents and the characteristics of the purchases made by the respondents (i.e., channel selection). Our data highlights the dominance of buy online, home delivery within the channel selection with more

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than three quarters of respondents utilizing that purchasing channel. A noteworthy observation is the roughly 15% of respondents who utilized the BOPS channel which is growing in popularity,

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and between 2-6% of respondents utilized buy in-store with home delivery option. After the removal of traditional in-store purchases as a channel option, the high percentage of respondents using buy online, home delivery is consistent with expectations (Bell et al., 2014; Gao and Su, 2016a). BOPS and buy in-store, home delivery are still relatively new channels in the omnichannel retail environment that do not have the same market share as the more established online, home delivery and in-store channels. Additionally, since we generalize the results to customer

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perception on channel selection intention, the findings show the general influence of channel characteristics and therefore should not be influenced by the actual channel selected in their most

Table 1: Characteristics of respondents and purchases. Response

Age

Under 18 18-24 25-34 35-44 45-54 55-64 65 or older

Characteristics of Customers’ Channel Selection Channel Selection Buy online, pick up in-store Buy online, home delivery Buy in-store, home delivery

43.9 56.1

66.3 33.7

0.0 12.6 44.0 20.6 11.4 8.8 2.6

17.6 76.5 5.9

0.0 24.1 38.4 28.1 7.4 2.0 0.0

14.2 83.1 2.7

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4.4 Data Analysis

U.K. Respondents Percentage

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Demographic Characteristics of Respondents Gender Female Male

U.S. Respondents Percentage

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Variable

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recent purchase (the product they considered when filling out the survey).

Based on previous studies (e.g., Xu and Gursoy, 2015), we conducted the data analysis through a two-step Structural Equation Modeling (SEM) approach with maximum likelihood estimation.

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The first step is Confirmatory Factor Analysis (CFA) to assess the measurement properties. Then, after confirming a desirable measurement model, we tested the interrelationships between the

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latent variables in the model through SEM.

5. RESULTS

5.1 Measurement Model Table 2 shows the measurement properties of the model generated through CFA for both U.S. (shown in numbers without parentheses) and U.K. samples (shown in numbers in parentheses).

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All average variance extracted (AVE) values are greater than the ideal cutoff of 0.50 (ranged from 0.52 to 0.71). To assess discriminant validity, the squared correlation between the constructs and the AVE scores were compared. All the correlations in the measurement model

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were less than 0.85, indicating the desirable discriminant validity. In addition, the construct reliability for each latent variable fell between 0.81 and 0.93, which were greater than the

minimum cutoff value of 0.70. The model fit indices from the CFA results for U.S. sample are as

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follows: χ2 =1598.89 (df = 566, p <0.001), RMSEA = 0.06, RMR = 0.05, SRMR = 0.05. The

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model fit indices from the CFA results for U.K. sample are as follows: χ2 =938.77 (df = 390, p <0.001), RMSEA = 0.06, RMR = 0.05, SRMR = 0.06. These indices all indicate the data fit the measurement model well. 5.2 Structural Model

The desirable measurement properties found in Section 5.1 allow us to examine the

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interrelationships between the constructs through a structural equation model. In Figure 2 the standardized path coefficients of the model for both U.S. (shown in numbers without parentheses) and U.K. samples (shown in numbers in parentheses) are presented. The fit indices from U.S.

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sample indicate that the data fits the model reasonable well ( χ2 =1618.83 (df = 574, p <0.001),

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RMSEA = 0.06, RMR = 0.05, SRMR = 0.05). The fit indices from U.K. sample also indicate that the data fits the model reasonable well ( χ2 =1640.55 (df = 444, p <0.001), RMSEA = 0.06, RMR = 0.06, SRMR = 0.05). As can be found from Figure 2, the results from U.S. and U.K. customers are quite consistent: all paths are significant except the path from channel convenience to perceived risk, which indicates an insignificant impact.

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Table 2: Measurement model Items

SFL*

AVE

Channel Transparency

The expected date of receipt of the product is clear when purchasing through this channel. The delivery information is readily available when using this channel. I know when my order has been received using this channel. I know when my order has been shipped or is being compiled using this channel. I know when my order has been delivered or is ready to be picked up in this channel.

0.66# (0.74)+ 0.72 (0.75) 0.81 (0.73) 0.78 (0.68) 0.81 (0.71)

0.57 (0.52)

Channel Convenience

Using this channel, I can shop anytime I want.

It is easy to learn about this product using this channel.

0.73 (0.74) 0.52 (0.74) 0.82 (0.71) 0.77 (0.73)

Channel Uniformity

The seller provides all shipping and tracking information with this channel. I can contact the seller directly with regard to any transaction issue in this channel. The seller will handle any issues directly when using this channel. The seller is responsible for the entire transaction process.

Product Price†

Relative to an average product I buy, this product is expensive. Relative to the purchase price of the product, the shipping cost using this channel is expensive. I am willing to pay more to get this product quicker through this channel.

Modified from Jiang et al. (2013)

0.74 (0.74) 0.71 (0.67) 0.82 (0.81) 0.66 (0.71)

0.54 (0.54)

0.82 (0.82)

Modified from Martin et al. (2014)

0.88

0.71

0.88

Modified from Bettman (1973a)

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0.95 0.67

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*

Modified from Otim and Grover (2016)

0.81 (0.82)

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It is easy to search for the product using this channel.

Source

0.52 (0.53)

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Using this channel, I can shop anywhere I want.

Reliability 0.87 (0.85)

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Construct

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SFL = standardized factor loading. Numbers without the parentheses are from the U.S. sample. + Numbers in the parentheses () are from the U.K. sample. † In the U.K. sample, product price is measured by the actual product price (in GBP), and therefore there is no SFLs for the U.K. sample. #

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Table 2 (cont.) Items

SFL*

AVE

Perceived Behavior Control

It would be very easy for me to use this channel if it is available. Whenever I want, I can easily buy the product through this channel if it is available. When buying the product, I have very much control over my ability to choose this channel if it is available. When buying the product, there are not many external influences that may prevent me from choosing this channel if it is available.

0.82# (0.74)+ 0.82 (0.71) 0.83 (0.75)

0.60 (0.53)

I am uncertain about the delivery of my order using this channel. I am concerned that the customer service in this channel will be difficult to talk to. I am worried that I may have to return the product using this channel. I am concerned that the product will not be delivered by the date I need the product when using this channel. It is my first time using this channel, therefore I am unsure about the performance of the channel.

0.76 (0.74) 0.78 (0.81) 0.71 (0.70) 0.78 (0.81)

When selecting this channel, shipping and handling cost was a major consideration. The marked price of the product was the cheapest when utilizing this channel. The total price (product + shipping + handling) was the cheapest when utilizing this channel. I would choose this channel in the future.

I would encourage family and friends to choose this channel in the future. I would recommend that people choose this channel in the future. I would list this channel in the future as one of my top options. I would choose this channel in almost every situation. I would share my positive attitude about choosing this channel to people in the future. I would purchase products using this channel in the future. I would spread positive word of mouth about this channel to my friends.

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Channel Selection Intention

Source Modified from Ajzen (2002) and Kang et al. (2006)

0.52 (0.55)

0.85 (0.86)

Modified from Wang and Hazen (2016)

0.66

0.85

Modified from Fassnacht and Unterhuber (2016)

0.64 (0.52)

0.93 (0.90)

Modified from Chang and Chen, (2008)

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Channel Price Advantage†

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Perceived Risk

0.61 (0.71)

Reliability 0.86 (0.82)

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Construct

0.57 (0.64) 0.66 0.87

0.88

0.81 (0.66) 0.84 (0.73) 0.86 (0.72) 0.82 (0.75) 0.62 (0.74) 0.80 (0.75) 0.80 (0.71) 0.82 (0.73)

*

SFL = standardized factor loading. Numbers without the parentheses are from the U.S. sample. + Numbers in the parentheses () are from the U.K. sample. † In the U.K. sample, channel price advantage is measured by the price gap (in GBP) between channels, and therefore there is no SFLs for the U.K. sample. #

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Figure 2: Results of the structural model.

Channel Transparency

Perceived Behavioral Control

-0.64*** (-0.34**) 0.44*** (0.32**) -0.06 (-0.16)

Perceived Risk

0.10*** (0.79***)

Channel Price Advantage

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Channel Uniformity

-0.10*** (-0.87***)

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Channel Convenienc e

0.86*** (0.29**)

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0.42***# (0.68**)+

-0.19*** (-0.41**)

-0.19*** -0.09** (-0.07***) (-0.03**)

Channel Selection Intention

0.13*** (0.11**)

-0.10*** (-0.05**)

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Product Price

# +

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*p < 0.1, **p < 0.05, *** p < 0.01

Numbers without the parentheses are from the U.S. sample. Numbers in the parentheses () are from the U.K. sample.

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6. DISCUSSIONS

6.1 The Influence of Channel Transparency, Convenience, and Uniformity on Perceived Behavioral Control and Perceived Risks The results from our structural equation model support hypotheses 1a and 1b. There is a positive impact of channel transparency on perceived behavioral control and negative impact on perceived risks. Channel transparency in the omni-channel retail environment can keep customers informed during each status of the transaction: order received, shipped, and delivered 26

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or ready to pick up. Thus, we find a channel with higher channel transparency can make customers feel at ease and in more control during the purchase process using this channel, which enhances their perceived behavior control. Additionally, in the omni-channel retail environment,

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customers may not be familiar with some of the new emerging channels (e.g., BOPS). Therefore, by providing a more transparent process, omni-channel retailers can alleviate customers’ frustration during the product fulfillment process and reduce their perceived risk.

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Our results presented in Section 5.2 also support hypothesis 2a, indicating that channel convenience positively influences customers’ perceived behavioral control. These results stem

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from the flexibility of searching and shopping for products when and where a customer wants. This reduces the external factors preventing a customer from using this channel and therefore increases their sense of control.

Somewhat surprisingly, our results do not support hypothesis 2b. Channel convenience

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does not have a significant impact on reducing the perceived risk. There are two possible explanations. First, the higher channel convenience can be a result of adoption of new technology, which becomes the new source of perceived risk because customers may be lacking

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the knowledge and capability of adopting the new shopping channel and its technology. Second, although higher channel convenience can save customers time and effort, it may also reduce the

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hedonic aspects of exploring the channel, which decreases customer enjoyment, and thus they perceive less usefulness and no perceived risk reduction. Thus, the results show a negligible impact of channel convenience on perceived risk in an omni-channel retail environment. Our results also support hypotheses 3a and 3b. Channel uniformity positively influences

customers’ perceived behavioral control, and negatively influences the perceived risk. High channel uniformity ensures the seller, fulfiller, and post-purchase service provider are consistent,

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which provides customers a single transaction-based contact point for any communications required during the post-purchase process. The survey results showed that customers are more comfortable when the entire purchase and fulfillment process is managed by the seller because it

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eliminates any intermediaries and the potential confusion that they can cause. As a result, they have more confidence and have more perceived behavior control. In addition, channel uniformity

of-contact which in turn reduces their perceived risk.

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can help customers build the relationship with the seller and provides a clear and known point-

6.2 The Moderating Effect of Price on the Influence of Channel Transparency,

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Convenience, and Uniformity on Perceived Risk

The results support hypotheses 4a, 4b, and 4c, in which we find that a high product price enhances the influence of channel transparency, convenience, and uniformity on perceived risk. A high product price increases the potential financial loss associated with a purchase, therefore

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customers have a higher desire to search for information prior to purchasing, which emphasizes the need for channel transparency when the product price is high. In addition, the impact of channel convenience on perceived risk is higher when the product price is high. This is because

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higher channel convenience can reduce customers’ stress and frustration when exerting more

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effort in the searching and purchasing processes due to the higher product price. Furthermore, we find the role of channel uniformity on reducing perceived risk is more significant when the product price is high due to its more significant function in minimizing customers’ uneasiness and worries of product and service failure for high-priced products. 6.3 The Influences of Customers’ Perceived Behavioral Control, Perceived Risk, and Channel Price Advantage on Channel Selection Intention

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The results from our structural equation model support hypotheses 5, 6, and 7, indicating that the perceived behavioral control, perceived risk, and channel price advantage influence customers’ channel selection intention. We find higher perceived behavioral control and lower perceived

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risk can make customers have less negative emotion such as frustration, worries, and uneasiness; and have more positive emotion such as confidence and trust, and thus enhance their channel selection intention. In addition, we find many customers are price-sensitive, which leads to the

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positive effect of channel price advantage—reflected by a lower product price, lower shipping cost, or lower total costs—on their channel selection intention.

7.1 Theoretical Implications

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7. THEORETICAL AND MANAGERIAL IMPLICATIONS

Our study has two main theoretical implications. First, our study extends the theory of planned behavior through examining the antecedents of perceived behavioral control in an omni-channel

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retail setting. The three antecedents we examined are channel transparency, convenience, and uniformity, which all examine characteristics of an omni-channel retail environment and illustrate the impact on a customer’s channel selection. The omni-channel retail environment

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provides a unique experience for customers, which in turn impacts the factors that influence their perceived behavioral control. Our study extends previous studies focusing on the transparency of

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the transaction process through characteristics such as pricing (e.g., Soh et al., 2006) by focusing on the availability of order tracking information provided by the channel (particularly in an online purchase setting using pickup in-store or home delivery). We find that customers value tracking their order during each stage of the purchase and fulfillment processes. This study also examines how channel convenience affects perceived behavioral control through the ease at which customers can search and purchase products at any time and from any location. Finally, to

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the best of our knowledge, we are the first to incorporate channel uniformity as an antecedent to perceived behavioral control. As we see an increase in third-parties taking on the fulfillment and after-sales service components of an order, the uniformity throughout the shopping process can

from consumers to have uniformity within a channel.

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vary dramatically retailer to retailer and channel to channel. Our results emphasize the desire

Our second major theoretical contribution is the extension of previous studies regarding

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customer purchase intention (e.g., Verhagen and Van Dolan, 2009) by examining channel

selection intention. In the traditional shopping environment, including both online and offline

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shopping, customers need to make two key decisions: what to buy (product choice), and where to buy (vendor choice). However, in an omni-channel retail environment, customers have another question: how to buy (channel choice). This study reveals the reasoning process of customers’ channel selection decision. The channel selection decision is influenced by both internal and

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external factors. The external factors (i.e., channel characteristics) are trigger factors. The channel characteristics: channel transparency, convenience, and uniformity affect customers’ channel selection intention indirectly through the internal factors of customers’ perceived

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behavioral control and risk. Meanwhile, channel price advantage affects customers’ channel selection intention directly. Product price strengthens the role of the beneficial channel

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characteristics in enhancing positive perception and alleviating negative perception. Our study reveals customers’ channel choice decisions can be related to the product choice decision through product price and the vendor choice decision through channel uniformity, which reveals the consistency in the seller and fulfiller.

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7.2 Managerial Implications As omni-channel retailers introduce more integrated and interactive channel to attract more demand, they introduce distribution, inventory management, and information system challenges

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in the development of a dynamic omni-channel supply chain. Prior to developing an integrated and interactive omni-channel supply chain, retailers must understand the omni-channel customer, particularly, their perception toward different aspects of the omni-channel retail environment.

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Our study focuses on three dimensions of customer perception in an omni-channel retail setting: channel transparency, channel convenience, and channel uniformity. All three dimensions have

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an impact on customers’ perceived behavioral control and impact perceived risk, which in turn, with channel price advantage influence customers’ channel selection intention. Generally, omnichannel retailers should make efforts in enhancing channel transparency, convenience, uniformity, and price advantage.

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To improve channel transparency, omni-channel retailers should make efforts to ensure customers’ awareness of the order status at each stage of the transaction and fulfillment process, which includes order receiving, handling, shipping, and arrival. Retailers should particularly

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focus on two improvements to achieve higher channel transparency: providing anticipated stage completion time and real-time updates; and use multiple communication channels including

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emails, text messages, phone calls, or through online account to let customers have access the information anytime and anywhere to feel the process is in control. To make the channel more transparent, information sharing efforts should be

implemented not only between companies and customers, but also between different providers in each channel. Channel transparency improvement actions should be implemented together with the actions to enhance channel convenience and with the consideration of channel uniformity.

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The enhanced channel convenience encourages customers to search and purchase, and thus omni-channel retailers also need to ensure transparency from the very beginning stages of the customers’ shopping process: searching. For example, Walmart.com provides detailed

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information not only for the products and purchase process (related to information of channel convenience), but also for the seller and fulfiller (related to information of channel uniformity). This makes customers aware of who should be contacted and the service provider in each stage

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of their ordering process (and thus enhances channel transparency).

Through the introduction of additional purchasing channels, an omni-channel retailer is

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increasing customer convenience by increasing flexibility of purchasing and delivery options, increasing the efficiency of the buying process, and decreasing the time and effort required to complete the transaction. The cores of improving channel convenience is higher flexibility (search and purchase at anytime and anywhere) and less effort (lower hassle costs of search and

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purchase).

Omni-channel retail customers desire channel uniformity throughout their purchase experience. Unfortunately, for many retailers, complete channel uniformity is difficult, if not

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impossible to achieve. Oftentimes, retailers will use third-party logistics companies (e.g., FedEx, UPS) to handle the distribution of online orders. In such scenarios, the omni-channel retailer can

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minimize the customers’ perceived risk from the lack of uniformity by using a third-party company with a strong brand, reputation, and online ratings. Additionally, channel uniformity can also be achieved indirectly through consistent usage

of the same third-party company for a stage of the purchasing process (e.g., distribution). This allows for the development of a long-term strategic relationship and consistent quality and customer service practices between the seller and fulfiller. In situations where the seller, fulfiller,

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logistic provider, post-service providers are different, companies should let customers know this in advance, and provide detailed contact information for each service provider. For example, Walmart.com provides the fulfiller’s name, contact information, and refund policy to their

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customers on their webpage. In summary, when it is not possible to improve all three channel characteristics in this study due to resource limitations, omni-channel retailers should try to

improve at least one or two characteristics to enhance customers’ positive perception and remedy

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the negative perception brought by other low-performing channel characteristics.

The improvement opportunities seen above can enhance the customers’ perceived

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behavioral control and reduce customers’ perceived risk for an omni-channel retailer. The enhanced perceived behavioral control and reduced perceived risk can assist customers enhance the 4Cs (certainties, confidence, conformability and controllability) of purchasing using the channel, and alleviate the worries, frustration and impatience during the searching, purchasing,

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and fulfillment processes.

Our results show that a higher product price increases the influence of channel transparency, convenience, and uniformity on reducing customers’ perceived risk. This prompts

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companies to further emphasize these channel attributes when product prices are high. Customers worry more about purchasing large ticket items and are more eager to receive these

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products. Omni-channel retailers should make corresponding efforts and input appropriate resources when the product price is high to reduce customers’ perceived risk through channel transparency, convenience, and uniformity. The model results also found that price discrepancies between channels influence

customers’ purchase intention. This shows that today’s customers are still price-sensitive, and the channel selection decision can be price-oriented. This provides a strategic opportunity for omni-

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channel retailers. Specifically, they can create channel price advantages for targeted channels. The targeted channels could be the most cost effective for the retailer (e.g., BOPS is often free, where there may be a shipping fee if customers get the product delivered to their home) or a new

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channel for the retailer that needs a boost in demand. Regardless of motivation, channel price

channel selection intention. 8. CONCLUSIONS AND EXTENSIONS 8.1 Conclusions

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advantage can be strategically implemented by omni-channel retailers to modify customers’

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Via an empirical analysis through surveys of U.S. and U.K. customers, we find the three antecedents of customer perceived behavioral control in an omni-channel retail setting: channel transparency, channel convenience, and channel uniformity. They all positively influence customer perceived behavioral control. In addition, we find that channel transparency and

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uniformity positively influence customers’ perceived risk, while channel convenience does not have a significant impact. Our results show higher product price increases the influence of channel transparency, convenience, and uniformity on reducing customers’ perceived risk.

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Furthermore, we find customer perceived behavioral control and channel price advantage have a positive impact, and perceived risk has a negative impact on customer channel selection intention

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in omni-channel retail environment. Our study provides an understanding of customer perception toward omni-channel retailers and the influential factors of customer channel selection intention in an omni-channel retail setting. 8.2 Extensions

This study has several limitations. First, we did not differentiate a customer’s intention to shop in the various purchase channels nor the reasons for purchase in an omni-channel retail

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environment. Future studies can extend our study by examining the influential factors of customer purchase intention in each channel offered by an omni-channel retailer. Second, our study only examined three channels, two of which were online buyers (with home delivery or

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pick-up in store options). Future studies can cast a broader net to collect data on customers’ usage and perceptions across different purchasing channels. Third, our study only targets

respondents from the United States and United Kingdom. The exploration of customers from

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other countries that do not have a developed omni-channel retail environment can be interesting. In addition, the exploration of the impact of customer characteristics (e.g., demographics) on

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customer channel selection intention can also be interesting. An exploration of external characteristics (e.g., retail sectors) and their impact on customer channel selection intention can be worthwhile. Fourth, a comparative study examining the different perceptions of the omnichannel retail environment from the service provider and customer perspectives would be

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interesting. They may have different perceived quality, value, and risks.

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Appendix A Pre-Questions in the Survey to Online Customers

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Our research focuses on three omni-channel shopping channels that are commonly used: (1) buy online, pick up in store, (2) buy online, home delivery, or (3) buy in store, home delivery. Please recall your most recent shopping experience using one of these three channels. During this experience, you may have completed the shopping process with or without purchasing the product. In the rest of the survey, we use “channel” to refer to the channel you selected above: (1) buy online, pick up in store, (2) buy online, home delivery, or (3) buy in store, home delivery.

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Pre-Question 1: The product you most recent shopped or searched is: Pre-Question 2: Did you buy it? A. Yes B. No Pre-Question 3: Why did you buy / search the product: A. I needed it B. I wanted it C. Gift to others D. Other reasons, please specify: Pre-Question 4: Which of the above three channel options did you use or plan to use? Channel: 1 2 3 Pre-Question 51: What is the price (in GBP) of the product you most recently shopped or searched for? ____________ Pre-Question 6: What channels were available to purchase this product (choose all that apply): 1 2 3

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Please provide the following price information (in GBP) for your product in Channel (1) buy online, pick up in store2: Pre-Question 7: Marked price: __________ Pre-Question 8: Shipping and handling fees: __________ Pre-Question 9: Total price: __________

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Please provide the following price information (in GBP) for your product in channel (2) buy online, home delivery3: Pre-Question 10: Marked price: __________ Pre-Question 11: Shipping and handling fees: __________ Pre-Question 12: Total price: __________

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Please provide the following price information (in GBP) for your product in channel (3) buy in store, home delivery4: Pre-Question 13: Marked price: __________ Pre-Question 14: Shipping and handling fees: __________ Pre-Question 15: Total price: __________

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Pre-Questions (PQs) 5-15 are asked to U.K. customers only. The original survey distributed to U.S. customers does not include PQs 5-15. PQs 7-9 will only show if the participant chooses channel 1 in PQ 6. 3 PQs 10-12 will only show if the participant chooses channel 2 in PQ 6. 4 PQs 13-15 will only show if the participant chooses channel 3 in PQ 6. 2

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Highlights: We identified three antecedents of channel selection intention.

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We found three omni-channel attributes that influence perceived behavioral control.

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Two of the three omni-channel attributes also influence perceived risk.

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Product price increases the influence of channel attributes on perceived risk.

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