Profiling ROPO Shoppers: Shopping Value Perspective

Abstract

Today’s consumers are multichannel shoppers, that is, they have many channel options for searching product-related information and purchase. This study focuses on one such form of multichannel shopping behavior of online search and offline purchase sequence known as ROPO (research online and purchase offline) behavior in the post-COVID-19 pandemic context. As shoppers return to stores post-COVID-19 pandemic, understanding this ROPO behavior will help retailers to develop strategies to meet their needs. This study attempts to profile the shoppers showing ROPO behavior based on the theory of perceived value of shopping, that is, shoppers select the channels at each stage of shopping that provide maximum value considering the various costs associated with those channels. Six drivers of ROPO behaviors are confirmed based on the sample of 526 respondents using confirmatory factor analysis. Furthermore, three distinct segments of shoppers—risk averters, value maximizers, and convenience seekers—showing ROPO behaviors are identified using k-means cluster analysis. Knowledge of these distinct shopper segments will provide managerial insight for retailers to provide an excellent shopping experience to their customers during the post-COVID-19 pandemic.

Keywords

ROPO behavior multichannel retail cluster analysis R programming shopping value

Introduction

Today, customers are multichannel shoppers who combine several channels simultaneously to maximize the benefits (Ewerhard et al., 2019; Hussein & Kais, 2020; Jo et al., 2020; Verhoef et al., 2007); for example, consumers can search online channels for various product-related information and then try out and purchase products in physical stores (Jo et al., 2020). This behavior is known as ROPO (research online and purchase offline) behavior, popularly known as webrooming (Chiu et al., 2011; Kleinlercher et al., 2020). ROPO is one of the dominant behaviors of shoppers in today’s retail environment (Chiu et al., 2011; Kleinlercher et al., 2020). As per the statistics published by Statista (2019), 56% of shoppers who start their shopping process online complete in physical stores. Similarly, according to Retail Perception (2014), 88% of shoppers search for information online before purchasing in physical stores. The Asia-Pacific region is also leading the growth of the same. According to a survey conducted by Transcosmos Inc. (2019), 39% of respondents in Manila (Philippines), 37% of respondents in Mumbai (India), 30% of respondents in Bangkok (Thailand), and 27% of respondents in Singapore said that they frequently searched products online before purchasing offline.

In the present context of the COVID-19 pandemic, according to the report published by Shopify (2022), ROPO behaviors of shoppers grow in popularity as stores and malls have begun to re-open following an increased rate of vaccination. Following the extended stay at home, consumers are craving to get back to shopping, especially browsing in person (Sharma, 2021). According to the survey conducted by Shopify (2022), 54% of consumers indicated that, over the next year, they are likely to look at a product online and buy it in store. A study conducted by Rossolov et al. (2022) also confirmed that most people are ready to return to in-store shopping in post-COVID-19 times. At the same time, as consumers search for product-related information online before going to store, consumers can spend less time in stores and can make a targeted and efficient purchase (Lai, 2021). Thus, ROPO behavior can reduce the risk of COVID-19 contamination.

Although ROPO behavior is rapidly becoming prevalent across the globe, profiling shoppers showing ROPO behavior has not been given much attention (Kleinlercher et al., 2020). Only a few studies have been conducted to understand this phenomenon (Aw, 2019, 2020; Fernández et al., 2018; Flavián et al., 2019; Kleinlercher et al., 2020). If we consider India, one of the emerging markets in the Asian continent, there is a dearth of such studies, and research on the same is becoming more and more important (Arora & Sahney, 2017; 2018; 2019; Hussein & Kais, 2020). In this regard, it is imperative for retailers to understand the profile of shoppers showing ROPO behavior in the post-COVID-19 pandemic.

Based on the foregoing, the aim of this article is twofold: (a) identify the drivers of ROPO behavior of shoppers and (b) profile the ROPO behavior of shoppers in the post-COVID-19 pandemic.

The remainder of the article encompasses the following sections: the second section presents a review of the relevant literature and theoretical foundation for hypothesis development. In the third section, the data source, variables, and statistical method adopted for analysis are explained. The fourth section elucidates the findings of the analysis with conclusions. Finally, in the fifth section, we discuss the theoretical and managerial implications of the study. Limitations and recommendations for future research are discussed in the last section.

Literature Review and Hypotheses Development

Today, customer journey is not a single-channel liner but merges across channels and complex. Customer purchasing process, from search to purchase, is not confined to physical stores or online stores only but takes advantages of both channels to make smart purchases (Flavián et al., 2019; Shi et al., 2020). Perceived values offered by different channels at different stages of the purchase process are driving the ROPO behavior of shoppers. Past studies have recognized that shoppers’ behavior is motivated by their perceived values of shopping—outcomes derived from the shopping process (Babin et al., 1994). Customers are value maximizers who evaluate various alternatives in terms of their benefits and costs and select the one that provides them with the overall greatest value (Zeithaml, 1988). Consumers’ perceived shopping value has been extensively studied in the retailing literature both for online and offline shopping contexts (Hsiao et al., 2012), but the focus so far has been almost exclusively on the single-channel environment (Hure et al., 2017). The concept of perceived shopping value is also important in the context of multichannel retailing (Dholakia et al., 2010). However, it is not captured appropriately in the multichannel context (Hure et al., 2017). To fill this gap, in this study, the perceived shopping value framework is adopted to understand the profile of ROPO shoppers.

Shoppers select the retail channels at each stage of shopping that provides maximum value considering the various costs associated with those channels (Aw, 2019; Hussein & Kais, 2020; Kabadayi et al., 2017). Some channels are more attractive to customers than others depending on the perceived value offered by channels (Kleinlercher et al., 2020). Multichannel shoppers maximize their values by combining the use of different channels in a shopping trip (Heitz-Spahn, 2013). Consumers may use one channel for information search and evaluation but purchase from another based on the benefits they seek (Hussein & Kais, 2020). Different shopping channels offer different values to customers. Table 1 summarizes the positive and negative values received by consumers shopping in online and physical stores based on previous studies (Aw, 2020; Chiu et al., 2011; Flavián et al., 2016; Forsythe & Shi, 2003; Heitz-Spahn, 2013; Noble et al., 2005; Patel & Sharma, 2009; Rohma & Swaminathan, 2004; Schröder & Zaharia, 2008; Verhoef et al., 2007).

Shoppers who engage in ROPO behavior may try to maximize the values offered by both online and offline channels. Online channels provide fast and easy access to a vast amount of information with online reviews of products at any time and compare various brands without leaving the home (Flavián et al., 2016; Li et al., 2019; Verhoef et al., 2007; Wang et al., 2018). Against this positive value of convenience, consumers found it inconvenient to return products purchased online or cancel an order once placed online compared to physical stores (Allred et al., 2006). At the same time, consumers found it difficult to evaluate the quality of products online as they cannot touch or see the same (Aw, 2020; Peck & Childers, 2003). This will motivate consumers to visit physical stores to inspect the products before purchase (Arora & Sahney, 2017). Therefore, these positive and negative values offered by online and offline channels motivate consumers to display ROPO behavior—search for products online but purchase in physical stores.

Table 1.

Values Offered by Online and Offline Channels.

Positive values offered by online channel Easy access to information from any place 24 × 7 access of information Usefulness of online reviews Easy comparisons of various brands Safe against COVID-19 contamination	Negative values offered by online channel Perceived risks (financial risk, delivery risk, and privacy risk) Delivery charges Delayed delivery of products purchased Lack of see, feel, and try of products Difficult to return or exchange product.
Positive values offered by physical stores Socialization with friends and family members Assistance of sales staff Immediate possession of products See, feel, and try of products Reliable delivery Easy to return or exchange product	Negative values offered by physical stores Need to travel to store Hard to locate products in store Crowd in the store Waiting in checkout line Unavailability of products Fixed timing of opening and closing of stores Chances of COVID-19 contamination

Source: Compiled by the author from various sources.

Despite the growing importance of ROPO behavior in today’s retail environment, only a few studies examine this phenomenon in detail from a perceived shopping value perspective and profile the shoppers. One of the earliest studies to understand this emerging phenomenon is the study by Verhoef et al. (2007), and this study highlighted three motives for research shopping: search (information availability, search convenience, and search effort), purchase (service quality, after-sales service, purchase convenience, negotiation possibilities, purchase effort, and purchase risk), and search-and-purchase attributes (assortment, price level, promotion, and clientele). Based on two studies, Falvián et al. (2016) found that combining online search and offline purchase improves customers’ purchase intention, satisfaction with search process, and confidence. Arora and Sahney (2017) conceptually proposed the potential motivations of webrooming such as perceived ease of online search, attitude toward physical stores (immediate possession, feel and touch the products, and assistance of salespersons), and lack of trust in online shopping. Recently, Flavián et al. (2019) conducted three studies and concluded that webrooming makes consumers feel more confident and induces the feeling of smart shopping, and these feelings lead to more satisfaction than showrooming. Although all of these studies contribute substantially to our understating of ROPO shoppers, there is still a lack of comprehensive understanding of the characteristics of this segment of shoppers, especially in emerging markets.

Using an in-depth review of past studies, six drivers are identified in this study to understand the ROPO behavior of shoppers: maximize product information, maximize product quality, maximize shopping enjoyment, maximize convenience, maximize instant gratification, and minimize perceived risk. These six drivers are discussed in the following sections in greater detail with the help of previous studies to build the conceptual model.

Maximize Product Information

This study hypothesized that shoppers show ROPO behavior to maximize product information. Consumers comprehensively search for more information from various sources to make better decisions and reduce risk (Noble et al., 2005). The Internet is the sea of product-related information, and consumers can search enormous amounts of information easily in less time and effort (Bakos, 1997; Heitz-Spahn, 2013; Wang et al., 2018). In recent studies, Flavián et al. (2016) and Fernández et al. (2018) concluded that consumers search for more product information online before purchasing from physical stores. Noble et al. (2005) also concluded that consumers use the information available in both online and offline stores to evaluate the quality of the product. Another reason to search for information online is that product reviews available online (Flavián et al., 2016) mitigate consumer concern about product fit and/or quality concern (Li et al., 2019). Consumers perceive these online reviews more trustworthy than commercial advertising (Aw, 2020). Flavián et al. (2016) also concluded that online reviews positively influence shopping in physical stores. Hence, it is proposed in this study that shoppers show ROPO behavior to maximize product information.

H₁: Shoppers show ROPO behavior to maximize product information.

Maximize Product Quality

This study hypothesized that shoppers show ROPO behavior to maximize product quality. Consumers judge the quality of products by touching material attributes such as texture, temperature, weight, and hardness (Liu et al., 2017; Peck & Childers, 2003; Vieira, 2012). Consumers are less likely to purchase if they cannot touch the products (Pantojaa et al., 2020). However, it is difficult for consumers to evaluate the quality of products online as they cannot touch the product (Aw, 2020; Gupta et al., 2004; Peck & Childers, 2003). Such consumers visit physical stores to touch and feel the products and draw better quality judgments before purchasing products (Arora & Sahney, 2017; Dholakia et al., 2010). In addition to that, some consumers need the assistance of salespeople to identify the ideal product fit (Mehra et al., 2013). Such consumers would be reluctant to buy online and prefer to purchase offline—showing ROPO behavior to make better purchase decisions (Arora & Sahney, 2019). Hence, it is proposed in this study that shoppers show ROPO behavior to maximize product quality.

H₂: Shoppers show ROPO behavior to maximize product quality.

Maximize Shopping Enjoyment

This study hypothesized that shoppers show ROPO behavior to maximize shopping enjoyment. Shopping is not only the acquisition of goods, but it also leads to the enjoyment and pleasure shoppers feel while shopping (Babin et al., 1994; Kim et al., 2019; Schröder & Zaharia, 2008; Verhoef et al., 2007). Shopping enjoyment may influence customer patronization of online and offline channels (Verhoef et al., 2007). Konuş et al. (2008) found that multichannel shoppers derive more pleasure and enjoyment in shopping than single-channel shoppers. Consumers enjoy interacting and spending time with their friends, relatives, acquaintances, fellow customers, and sales staff at physical stores (Arnold & Reynolds, 2003; Schröder & Zaharia, 2008). In addition to that, consumers also enjoy bargaining with the salesperson in physical stores. Such enjoyment is not available in online shopping (Lee, 2017). Hence, based on the previous discussion, it is proposed in this study that shoppers show ROPO behavior to maximize shopping enjoyment.

H₃: Shoppers show ROPO behavior to maximize shopping enjoyment.

Maximize Convenience

Shoppers display ROPO behavior to maximize convenience, that is, purchasing products with minimum investment of time, physical effort, and mental effort (Schröder & Zaharia, 2008). On one hand, previous studies on online shopping motivation found that convenience of product-related information search is considered a key advantage of online shopping (Jiang et al., 2013). Consumers can search for information online anywhere without traveling to physical stores as the location becomes irrelevant in online information search (Jiang et al., 2013; Noble et al., 2005; Rohma & Swaminathan, 2004). On the other hand, there is some inconvenience related to after-sales services in purchasing online. Consumers found it more inconvenient to return products purchased online than products purchased at a physical store (Allred et al., 2006; Maity & Arnold, 2013; Ofek et al., 2011). It is also a hassle to cancel an order placed online (Allred et al., 2006). Against this backdrop, it is assumed that consumers find it convenient to search for information online but prefer to purchase in physical stores to avail fast and convenient after-sales services. Therefore, it is proposed in this study that shoppers show ROPO behavior to maximize shopping enjoyment.

H₄: Shoppers show ROPO behavior to maximize shopping enjoyment.

Maximize Instant Gratification

It is hypothesized in this study that shoppers show ROPO behavior to maximize instant gratification—immediate possession of products after purchase. The time to possess the products after purchase will be different across different shopping channels (Aw, 2019). After purchasing the products, consumers can receive them immediately if they buy them at a physical store. However, consumers will have to wait for product delivery if purchased online. Longer delivery time is one of the reasons that prevents consumers to buy online (Bhatnagar & Ghose, 2004). Forsythe and Shi (2003) found that shoppers hesitate to buy online due to the lack of immediate possession. In the similar line, Yan and Ghose (2010) found that delayed deliveries stop customers from buying online. Certain consumers visit physical stores only because of instantaneous delivery of products (Aw, 2019). Therefore, it is proposed in this study that shoppers show ROPO behavior to maximize instant gratification.

H₅: Shoppers show ROPO behavior to maximize instant gratification.

Minimize Perceived Risk

It is hypothesized in this study that shoppers show ROPO behavior to minimize the perceived risks and uncertainty resulting from their perception of negative consequences of purchases through a specific channel (Hussein & Kais, 2020; Schröder & Zaharia, 2008). Consumers’ decisions to purchase online or in physical stores are significantly influenced by their risk perception (Arora & Sahney, 2019; Burke, 2002; Forsythe et al., 2006; Gupta et al., 2004). Many past studies have documented that purchasing online is risky, and this risk perception is the main obstacle to the adoption of online shopping (Guru et al., 2020). Consumers may feel the risk of inability to judge the quality of products, not receiving the products or wrong product delivered, receiving fake products, delivery at a wrong place, misuse of credit/debit cards, and misuse of personal information (Guru et al., 2020; Schröder & Zaharia, 2008). The studies by Verhoef et al. (2007), Chiu et al. (2011), and Chiou et al. (2017) and recent studies by Arora and Sahney (2018, 2019) and Hussein and Kais (2020) have confirmed that consumers’ perception of risk purchasing online drives customers toward physical stores to purchase after collecting information online. Therefore, it is proposed in this study that shoppers show ROPO behavior to minimize perceived risk.

H₆: Shoppers show ROPO behavior to minimize perceived risk.

In conclusion, six factors are identified as drivers of ROPO behavior: maximize product information, maximize product quality, maximize shopping enjoyment, maximize convenience, maximize instant gratification, and minimize perceived risk (Figure 1). The following section describes the empirical confirmation of these six drivers and, finally, profiles the shoppers showing ROPO behavior.

Figure 1.

Source: The author.

Empirical Study

Measures

A structured questionnaire was developed to collect data. The questionnaire consisted of three sections: The first section contained questions to identify respondents showing ROPO behavior. The second section of the questionnaire contained various items measuring the six factors of ROPO behavior identified for the study. These items were developed based on previous studies and adapted to meet the context of the current study. Please refer to Table 2 for details related to constructs used in the study. All items were measured on a 9-point Likert scale, anchored at 1 = “strongly disagree” and 9 = “strongly agree.” The questionnaire was pre-tested with 12 participants in order to check and improve the understandability of questions.

Table 2.

Constructs and Their Reliability Measures.

Constructs (source)		Cronbach’s Alpha
Maximize product information (Arora & Sahney, 2019; Aw, 2020; Noble et al., 2005)		0.953
PI1:	I spend a lot of time looking for information about product and brand before I make a purchase.
PI2:	I often search information related to product online.
PI3:	I can get useful information about product online.
PI4:	When I search information of product both online and offline, I can get much information of product.
PI5:	Online reviews of products are useful for me to purchase good product.
Maximize product quality (Aw, 2020)		0.872
PQ1:	It is important for me to have best quality of product I purchase.
PQ2:	It is important for me to see the product to evaluate its quality.
PQ3:	It is important for me to touch the product to evaluate its quality.
PQ4:	I can evaluate quality of products by seeing it on websites.
PQ5:	I can evaluate quality of product by reading its information on websites.
PQ6:	I feel the need to have salespeople show me how products work.
Maximize shopping enjoyment (Arora & Sahney, 2019; Aw, 2020; Schröder & Zaharia, 2008)		0.919
SE1:	I love shopping products in stores.
SE2:	I enjoy interacting with others when I shop in stores.
SE3:	I always need help of sales staff when I purchase products.
SE4:	I love to shop at stores with friends/relatives.
SE5:	I love to bargain with sales staff at stores.
SE6:	Searching product online is enjoyable.
SE7:	Shopping in the stores gives me opportunity to go out of home.
Maximize convenience (Arora & Sahney, 2019; Forsythe et al., 2006; Schröder & Zaharia, 2008)		0.947
MC1:	I can get product information online anytime I want.
MC2:	The product information is always accessible online.
MC3:	I can easily search various information related to products online.
MC4:	I can easily compare various information related to products online.
MC5:	I don’t have to leave home to search product information online.
MC6:	I can get good service of for product if I buy from store.
MC7:	I can easily return product if purchased from store.
MC8:	I can easily cancel the order if needed when purchase from store.
MC9:	I can easily exchange product if purchased from store.
Maximize instant gratification (Arora & Sahney, 2019; Noble et al., 2005)		0.980
IG1:	I would rather buy a product at store than order it online and wait for it to arrive.
IG2:	When I order a product I don’t want to wait for it to arrive.
IG3:	When I purchase a product, I want to use it immediately.
IG5:	I don’t mind ordering product online and waiting for the product to arrive.
Minimize perceived risk (Forsythe et al., 2006; Schröder & Zaharia, 2008)		0.927
PR1:	Shopping online can involve a waste of money.
PR2:	I feel that my credit/debit card number may not be secure if I purchase online.
PR3:	I might get overcharged if I shop online.
PR4:	Online shopping website will not protect my private information.
PR5:	I might not get what I ordered through online shopping.
PR6:	It is hard to judge the quality of product over Internet.
PR7:	I might not receive the product ordered online.
PR8:	Delivery may be sent to the wrong place.
PR9:	I might not receive an original product when shop online.

Source: The author.

Sampling and Data Collection Procedure

The study focuses on profiling shoppers showing ROPO behavior while purchasing apparels and related accessories as this product category has experiential as well as search attributes that motivate consumers to search online and draw them to offline stores to try out and purchase (Verhoef et al., 2007). Hence, it is expected that shoppers show ROPO behavior while shopping for apparels and related accessories. Data collection was performed via the online questionnaire method immediately after the end of the second wave of the COVID-19 pandemic, that is, in the months of July–August 2021. The questionniare was implemented in Google Form and distributed through various online channels like e-mails and social networks (e.g., WhatsApp and Facebook). From a methodological point of view, the selection of respondents was made on the basis of the availability and voluntariness of the participants. To qualify for the study, respondents were asked to indicate their likelihood to purchase apparels and related accessories in-stores after searching for information about them online using the rating scale: never, unlikely, likely, and almost certainly. Respondents whose response to the above question were likely and almost certainly are considered ROPO shoppers. A total of 565 respondents participated in the survey. Of these, only 526 responses were found to be complete and suitable for the study.

The demographic profile of respondents is presented in Table 3. The demographic profile reveals almost equal distribution of male and female respondents. The majority of respondents are younger than 30 years of age (43.35%) and have bachelor’s degree education (47.91%). The proportion of respondents in different income categories varied from 11.98% to 26.81%.

Table 3.

Demographic Profile of the Sample.

Gender	Male = 49.62%; female = 50.38%
Age	Less than 30 years = 43.35%; 31–40 years = 31.37%; 41–50 years = 18.82%; more than 50 years = 6.46%
Education	Lower than bachelor degree = 18.25%; bachelor degree = 47.91%; master degree or higher = 33.84%
Income (per month)	Less than ₹50,000 = 20.15%; ₹50,000–80,000 = 24.71%; ₹80,000–110,000 = 26.81%; ₹110,000–140,000 = 16.35%; more than ₹140,000 = 11.98%

Source: The author.

Note: ₹1,000 = USD 13.40 as on February 23, 2022.

Data Analysis

Confirmatory factor analysis (CFA) was performed to confirm the six factors identified as drivers of ROPO behavior. Prior to that, Cronbach’s alpha was used to purify the measurement scale for each of the constructs. All six drivers surpassed the reference point of Cronbach α = 0.70 (Nunnally & Bernstein, 1994) and found it to be reliable (please refer to Table 4).

Confirmatory Factor Analysis

CFA using the lavaan package of open-source R programming (Rosseel, 2012) was conducted to determine if the measurement items for each driver of ROPO behavior were loaded as predicted on the respective drivers. The items, not loaded meaningfully on the underlying drivers, were deleted. The final measurement model has adequate model fit indices (chi-square/df = 2.498; CFI = 0.968; RFI = 0.942; NFI = 0.947; IFI = 0.968; RMSEA = 0.053), which exceeded the recommended criteria commonly suggested in the literature (Fornell & Larcker, 1981; Hair et al., 2014).

Factor loadings, average variance extracted (AVE), and composite reliability (CR) were used to determine convergent and discriminant validity. As shown in Table 4, the factor loading of all measurement items was above 0.5 and significant at the 0.05 level. AVE values were above 0.5, and CR values were above 0.7, demonstrating convergent validity (Fornell & Larcker, 1981; Hair et al., 2014). Table 5 shows that the average AVEs of two constructs were far greater than the square correlation coefficient of the two constructs, supporting discriminate validity of the constructs in the study (Hair et al., 2014). Therefore, CFA results confirm the six drivers of ROPO behavior of shoppers.

Table 4.

Results of Confirmatory Factor Analysis.

Construct	Factor Loadings	AVE	CR
Maximize product information
PI1 PI2 PI3 PI4 PI5	0.908 0.907 0.902 0.919 0.848	0.806	0.953
Maximize product quality
PQ1 PQ2 PQ3 PQ6	0.881 0.863 0.909 0.918	0.798	0.940
Maximize shopping enjoyment
SE1 SE2 SE3 SE4 SE5 SE6	0.874 0.862 0.862 0.841 0.879 0.878	0.750	0.947
Maximize convenience
MC1 MC2 MC3 MC5 MC6 MC7 MC8 MC9	0.838 0.901 0.900 0.905 0.908 0.909 0.900 0.831	0.790	0.967
Maximize instant gratification
IG1 IG2 IG3 IG4	0.961 0.943 0.978 0.966	0.927	0.980
Maximize perceived risk
PR2 PR4 PR5 PR7 PR8 PR9	0.958 0.944 0.946 0.954 0.953 0.957	0.906	0.983

Source: The author.

Note: Items PQ4, PQ5, MC4, PR1, PR3, and PR6 are dropped from further analysis due to low factor loadings.

Table 5.

Result of Discriminant Analysis.

	PI	PQ	SE	MC	IG	PR
PI		0.052	0.035	0.025	0.033	0.023
PQ	0.806		0.106	0.044	0.004	0.053
SE	0.778	0.774		0.319	0.010	0.103
MC	0.798	0.794	0.770		0.004	0.075
IG	0.866	0.862	0.838	0.858		0.073
PR	0.856	0.852	0.828	0.828	0.916

Source: The author.

Note: Values below the diagonal show the average of AVEs between two constructs. Values above the diagonal show square correlation between two constructs.

Cluster Analysis to Profile ROPO Shoppers

The average summated scores from the above six drivers were used to conduct cluster analysis to profile shoppers showing ROPO behavior. Three clusters, each with 214, 177, and 135 respondents, respectively, were obtained by the K-means clustering method. Means and standard deviations for the three clusters identified are described in Table 6. Furthermore, ANOVA tests revealed that the six dimensions of ROPO behavior differed significantly across three clusters (All F values are significant). Demographic profiles of the three clusters identified are presented in Table 7.

Table 6.

Means and Standard Deviation of ROPO Behavior by Clusters Identified.

	Cluster 1: Risk Averters	Cluster 2: Value Maximizers	Cluster 3: Convenience Seekers	Total Sample	F
Maximize product information	6.025 (1.487)	7.409 (0.936)	7.179 (1.183)	6.787 (1.398)	68.68*
Maximize product quality	6.271 (1.355)	7.791 (0.807)	7.728 (0.977)	7.156 (1.321)	116.8*
Maximize shopping enjoyment	5.926 (1.303)	7.818 (0.728)	8.001 (0.681)	7.095 (1.389)	250.7*
Maximize convenience	6.073 (1.697)	7.736 (0.769)	8.060 (0.630)	7.142 (1.506)	142.9*
Maximize instant gratification	7.786 (1.554)	7.007 (2.081)	6.131 (2.415)	7.099 (2.087)	29.08*
Minimize perceived risk	7.671 (1.151)	7.885 (0.630)	3.091 (0.889)	6.568 (2.250)	1257*
Cluster size (%)	214 (40.68%)	177 (33.65%)	135 (25.67%)

Source: The author.

Note: *p < 0.0001.

Table 7.

Demographic Profile of Each Cluster.

	Cluster 1: Risk Averters (%)	Cluster 2: Value Maximizers (%)	Cluster 3: Convenience Seekers	Chi-square Test of Independence
Percentage of respondents	40.68	33.65	135 (25.67%)
Gender
MaleFemale	45.7954.21	44.0755.93	62.9637.04	13.05*
Age
Less than 3031–4041–50More than 50	36.9240.1916.356.54	32.7731.6429.386.21	67.4117.038.896.67	57.067*
Education
Lower than bachelor degreeBachelor degreeMaster degree or higher	19.1649.0731.77	24.8643.5031.64	8.1551.8540.00	15.133*
Income (in ₹)
Less than 50,00050,000–80,00080,000–110,000110,000–140,000More than 140,000	23.8327.5726.6414.487.48	26.0027.1124.2912.4310.17	6.6717.0430.3724.4421.48	43.842*

Source: The author.

Note: * The chi-square value is significant at the 1% level of significance.

Cluster 1: Risk Averters

This cluster comprises 43.68% of the total respondents. They are being mostly women (54.21%), young (77.11% are below the age of 40), and educated (80.84% have at least a bachelor’s degree). 78.04% of the respondents in this cluster are characterized by their moderate level of income (not more than ₹110,000). Risk averters show ROPO behavior to minimize the risk of online shopping and maximize instant gratification. From the parameter estimates, it is clear that the shoppers of this segment are most affected by their perceptions of online risk, specifically misuse of credit/debit cards, personal information, non-delivery of the products ordered online, and wrong delivery. Hence, they prefer to shop at physical stores to have immediate possession of their purchase and mitigate the risks of online shopping. The pattern, therefore, seems to be that perceived risks and instant gratification are positively correlated.

Cluster 2: Value Maximizers

This cluster represents 33.65% of respondents, including more women (55.93%), educated (75.14% of respondents have a bachelor’s degree or higher education), and equally distributed among age groups (32.77% are less than 30, 31.64% are between 31 and 40, and 29.38% are between 41 and 50). Additionally, this customers group is characterized by a moderate income level (77.4% of customers have an income not more than ₹111,000). The customers of these clusters are high on all the drivers of ROPO behavior. They show ROPO behavior to maximize quality of the product they purchase. And to have good quality products, they also want to maximize product information search. They also maximize convenience and want enjoyment in the shopping process. They also feel online shopping is risky; hence, they want to reduce the risk by shopping in physical stores so that they can have immediate possession of products. For these reasons, they are labeled as “value maximizers” in terms of quality, information search, convenience, enjoyment, reduced risk, and self-gratification.

Cluster 3: Convenience Seekers

This cluster comprises 25.67% of respondents. They are being mostly men (66.96%), very young (67.41 % are below the age of 30), highly educated (51.85% have a bachelor’s degree, and 40% have a master degree’s or higher). Additionally, this cluster is characterized by a high level of income (45.92 % of respondents have an income more than ₹110,000). Customers of this cluster show ROPO behavior to maximize convenience of shopping and shopping enjoyment. They search for product-related information online as it is easy to search and compare information online any time of the day even if they do not have to leave the home to search for product information. At the same time, they find it convenient to purchase products in physical store to get good after-sales service.

Managerial Implication

Retailers may benefit from the results reported here. The future growth of in-store retailing depends largely on understanding how potential customers will behave in the post-COVID-19 pandemic. In-store shopping is ramping up again, and shoppers will continue to exhibit ROPO behavior. According to the survey conducted by Shopify (2022), 54% of consumers indicated that, over the next year, they are likely to look at a product online and buy it in a store. Far more people research online before going to the store and thus make a targeted and efficient purchase to reduce COVID-19 transmission. This research offers vital insights for retailers to understand ROPO behavior of shoppers post-COVID-19 pandemic and help them develop successful strategies to provide a complete and seamless shopping experience to their customers. It reveals insights into how retailers maximize the value they create for their customers using multichannels. It is important to identify who are the shoppers showing ROPO behavior, and developing the strategy to meet their needs should be at the center of the retailer’s strategy (Jo et al., 2020). Little is known until now about ROPO behavior of shoppers in India, which constitutes the rapidly growing market. This study contributes to the work in this field by adding to the literature on multichannel shopping, especially by profiling the shoppers with such shopping behavior.

This study revealed the existence of three distinct segments of shoppers showing ROPO behavior. These segments are named risk averters, value maximizers, and convenience seekers. Knowledge of these distinct shopper segments is useful for retailers in crafting multichannel strategies. It is important for retailers to create a unified view of shopping from the perspective of purchase, return, and exchange irrespective of channels (Shi et al., 2020). For example, shoppers show ROPO behavior to reduce various risks of online buying. To mitigate these risks, retailers may also additionally offer facilities of home delivery or click-and-collect, that is, pick up of their orders placed online at the nearest physical stores. Similarly, online-purchased products can be returned and exchanged in physical stores. On the other hand, value maximizers show ROPO behavior to maximize the quality of the products, and they search for a vast amount of product-related information across channels to purchase the best quality products (Flavián et al., 2016). These product-related information including product reviews can be easily accessed online at no cost, which help customers to make better decisions in offline stores (Arora & Sahney, 2019). It is crucial for retailers to make sure that customers can access consistent and extensive product-related information across different channels. Shoppers should be encouraged to visit physical stores to touch and feel the products (Jo et al., 2020). In addition, physical stores can communicate and encourage shoppers to visit online stores (or mobile apps) to get more information about the products displayed in stores (Jo et al., 2020). For example, retailers in the USA, such as Hointer, Rebecca Minkoff, American Apparel, and Belk, launched apps that allow shoppers in-store to scan any products that instantly display product information, ratings and reviews, product videos, and many more (Insider Trends, 2016). Retailers in emerging markets like India may use in-store apps to promote ROPO behavior of shoppers for better experience. This study contributes to this relatively unexplored area of literature on ROPO behavior of shoppers post-COVID-19 pandemic and provides managerial insight for multichannel retailers.

Limitation and Future Research

In this last section, limitations of the study have been presented, which suggest directions for future research. First, this study is focused on ROPO behavior of shoppers while purchasing apparel and related accessories. Future research could explore this behavior while purchasing other products, and it would be interesting to see if the same results hold. Second, it is based on self-reported survey data in which participants answered the questions based on their memory of ROPO behavior. Such data may be inaccurate, and future research should adopt other methods of data collection. Result presented here is based on cross-sectional data; longitudinal data are likely to be needed in the future as retailing is continuously evolving day-to-day post-COVID-19 pandemic. Third, the data in this study were collected from only one geographical location, India, which prompts concerns about external validity. ROPO behavior of shoppers of other countries may not be similar to those in India. It would thus be useful to replicate this research in other regions and countries in order to enhance generalizability.

Footnotes

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Vipul Patel

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