Review Article: 2023 Vol: 27 Issue: 3
Sau K Leung, Brac University, Dhaka, Bangladesh
Citation Information: Leung, S.K. (2023) 24-hour online shopping: a study in indian college online shoppers. Academy of Marketing Studies Journal, 27(3), 1-11.
Online shopping is becoming increasingly popular around the globe. However, previous research has paid only limited attention to online marketing strategies according to different times of the day. The present study seeks to fill this gap by investigating the effects of diurnal preferences on online shopping satisfaction, attitudes and intention based on self-congruency theory. It was found that consumers with earlier sleep and wake times demonstrated greater sensitivity to financial and privacy risks while also expressing greater satisfaction with, possessing more positive attitudes toward, and exhibiting higher intention to use online shopping. These findings suggest that consumers shopping in the morning, afternoon, or evening have different needs that require different marketing and sales strategies to be met. Theoretical and managerial implications are therefore suggested.
Eveningness-Morningness, Online Shopping, Perceived Risk
Online shopping has become increasingly popular, with global online retail sales estimated to increase from 7.4% of total retail spending in 2016 to 8.8%, or $2,489 trillion US dollars, in 2018 (Saleh, 2017). Online retail sales in developed countries constitute a significant portion of the international retail market: of the total retail sales in 2017, the UK represented 15.6%, Norway 11.5%, Finland 10.8%, and South Korea 10.5% (Saleh, 2017). With the advance of digital devices and social media, developing countries have yielded the fastest growth in online retail sales. China alone is estimated to grow from representing 12% of total online retail sales in 2015 to 16.6% in 2018 (Saleh, 2017). India is estimated to grow from 1.7% in 2015 to 4.8% in 2019 (Images Retail Bureau, 2016).
Online shopping has gained increased popularity because of its convenience. Consumers can easily compare prices and access consumer reviews (Lifewire, 2017). They can also shop in a low-pressure environment while saving time, fuel, money, and energy (Ebay, 2017). Additionally, online shopping can offer more product choices to consumers (Lifewire, 2017). Online stores can also provide 24/7 availability, an advantage that is highly regarded by retailers (Ebay, 2017). Consumers can shop at their own pace and convenience online, allowing retailers to better meet their needs (Ebay, 2017). However, research investigating online shopping during different periods of the day is limited, despite the importance of 24/7 availability in online shopping. Given that 70% of the total online shoppers were aged 18-35 years old in India (Livemint, 2017), in which college students were popular online shoppers constituting an attractive market segment (Vaidya, 2017), the present study investigated the effects of diurnal preferences on customer satisfaction, attitudes toward, and intention to use online shopping in Indian college online shoppers.
Self-congruency theory posits that consumers prefer products/brands/services that are congruent with their own self-image (Belk, 1988; Forehand et al., 2002; Graeff, 1996; Kressmann et al., 2006; Sirgy, 1986; Sirgy et al., 1997) because these products/brands/services better express and symbolize their own personality traits and desires (Liu et al., 2012; McEnally & De Chernatony, 1999). Individuals normally have positive feelings about products/brands/services that project images closely matching their own personality traits; therefore, consistency in these images maintains those positive feelings, reduces uncertainty, and enhances the predictability of a consumer’s use and purchase of products/brands/services (Markus, 1977; Swann, De La Ronde, & Hixon, 1994).
Consumers tend to avoid using or purchasing products/brands/services that lie beyond “who they are” in their self-schema (Aaker, 1999). This schematic motive manifests itself as a stronger preference for the image of those products/brands/services that are congruent with consumers’ personality traits (Linville & Carlson, 1994; Tepeci, 1999) because this preference can consolidate and enhance self-concepts (Sirgy, 1982; Zinkhan & Hong, 1991).
The self-concept of an individual can be considered a continuum, ranging from ideal, actual, or social to the ideal social self (Tajfel & Turner, 1985). The actual self refers to how a consumer sees himself/herself, while the ideal self refers to how a consumer would like to see himself/herself, and the social self refers to how a consumer thinks others perceive him/her. Conversely, the ideal social self refers to how a consumer would like to be seen by others (Belch & Landon, 1977; Sirgy, 1982).
Self-congruence (products/brands/services that match self-image) enhances consumer attitude, satisfaction, intention to purchase, and use of self-services (Achouri & Bouslama 2010; Ekinci & Riley, 2003; Jamal & Al-Marri, 2007; Pradhan, Duraipandian, & Sethi, 2016). Conversely, self-incongruence (products/brands/services that do not match self- image) triggers negative self-image and avoidance of certain products, brands, or services (Hogg & Banister, 2001; Neale, Robbie, & Martin, 2016). Furthermore, self-congruence affects store choices and shopping experiences (Ha & Im, 2012; O’Cass & Grace, 2008). This study employs self-congruency theory as a framework to predict the effect of diurnal preferences on the relationship between perceived risk, attitudes toward, satisfaction with and the use of online shopping.
All individuals demonstrate different diurnal preferences termed morningness or eveningness, or simply morningness-eveningness, which refers to the spectrum of human circadian rhythms (Susman et al., 2007). Individuals who demonstrate a morningness preference prefer to go to sleep early at night and wake early in the morning, while those demonstrating an eveningness preference prefer to stay up late and wake later in the morning (Urbán, Magyaródi, & Rigó, 2011). Morningness individuals are most alert in the morning hours, whereas eveningness individuals are most alert in the afternoon or evening (Randler, 2008a). This biological clock is thought to be set according to genetic, psychosocial, and contextual factors (Susman et al., 2007). An estimated 40% of the population exhibit extreme morningness or eveningness, while 60% of the population, termed intermediate-type individuals, can be classified between morningness and eveningness (Mongrain, Lavoie, Selmaoui, Paquet, & Dumont, 2004).
Diurnal preference is considered a stable characteristic in humans. It is affected neither by ethnicity nor socioeconomic status (Paine, Gander, & Travier, 2006), but by a range of physiological, psychological, and social factors (Czeisler & Buxton 2010; Monk, 2005; Turek, 2000) such as circadian rhythms, homeostatic processes, light intensity, and work schedule (Czeisler & Buxton 2010; Monk, 2005; Zlomanczuk & Schwartz, 1999). Diurnal preferences are also related to adolescent behavior (Goldstein, Hahn, Hasher, Wiprzycka, & Zelazo, 2007), drug use and smoking habits (Díaz-Morales & Sánchez-López, 2008; Nakade, Takeuchi, Kurotani, & Harada, 2009; Wittmann, Paulus, & Roenneberg, 2010), food intake (Randler, 2008a), health-related behaviors (Schaal, Peter, & Randler, 2010), perceptions of one’s health (Paine, Gander, & Travier, 2006), and physical activity or inactivity (Schaal, Peter, & Randler, 2010). Diurnal preferences are related to different aspects of human behavior and, by extension, online shopping intention.
Intention to Use Online Shopping
The intention to use online shopping refers to how likely a customer is to patronize an online store again in the future (Chen, Chen, & Chen, 2009; Frambach, Herk, & Agarwal, 2003). Previous research has found that personalization (Ho & Bodoff, 2014; Pappas, Kourouthanassis, Giannakos, & Chrissikopoulos, 2016), message quality (Mun, Yoon, Davis, & Lee, 2013), shopping benefits (Xu, Dinev, Smith, & Hart, 2011; Lee, 2009), trust (Beldad, Jong, & Steehouder, 2010), and positive emotions (Kuo & Wu, 2012, Pappas et al., 2016; Verhagen & van Dolen, 2011) are antecedents to the continued use of online shopping. Cultivating factors that drive repeated use of online shopping in the form of retained customers can be more cost-effective for managers than acquiring new customers (Bhattacherjee, 2001).
Attitudes toward Online Shopping
Positive attitudes can enhance the use of and increase purchases from online retailers (Amaro & Duarte, 2014; Hsu, Yeb, Chiu, & Chang, 2006). Customers form attitudes, or the degree of favor or disfavor to a service (Dolharker & Bagozzi, 2002), from the evaluation of many different elements (Andreassen, 2001; Eastlick, Ratto, Lotz, & Mishra, 2012). Drivers of positive attitudes toward online shopping include detailed product descriptions (Kim & Lennon, 2008), online shopping knowledge and experience (Soopramanien, 2011), perceived benefits (Lee, 2009), and quality visual graphics. When customers have accumulated experience using a service, they can form a global attitude toward it (Parasuraman, Zeithaml, & Berry, 1994). Once a customer has formed a positive or negative attitude toward a company, product, or service, it is difficult to change (Curran, Meuter, & Surprenant, 2003).
Satisfaction with Online Shopping
Satisfaction is a psychological evaluation process in which the interaction between a consumer’s expectation and service/product performance affects the consumer’s attitudes (Chen, 2005; Lee & Joshi, 2006). When the performance is higher than the expectation, the customer will feel satisfied; if the reverse is true, he or she will feel dissatisfied (Chen, 2005). Understanding the antecedents to satisfaction is important as satisfaction is a driver of consumer loyalty (Yang & Peterson, 2004; Ju Rebecca Yen & Gwinner, 2003) as well as continued use of retailers (Marzocchi & Zammit, 2006), services (Cronin, Brady, & Hult, 2000), and technologies (Wang, 2012). In online shopping environments, factors that affect satisfaction include customer service, delivery performancne, information quality, merchandise attributes, product variety, reliability, transaction capability, and website design (Alam & Yasin, 2010; Lin & Sun, 2009; Liu, He, Gao, & Xie, 2008; Luo, Ba, & Zhang, 2012).
Perceived Risk and Online Shopping
When uncertainty arises, customers are likely to perceive risk (Forsythe & Shi, 2003; Michell & Harris, 2005). Perceived risk, defined as potential loss when desired outcomes are pursued (Ko et al., 2010), is a multidimensional construct. (Zhao et al., 2008) proposed eight dimensions of perceived risk: financial, psychological, performance, psychosocial, time/convenience, security, privacy, and physical. Because customers by and large do not have the opportunity to examine products nor engage in face-to-face interaction with sales persons in an online environment (Laroche, Yang, McDougall, & Bergeron, 2005), they might experience fear as to whether they will receive the right products and services (Mitchell, 1999). Customers may also perceive online payments as potentially insecure (Salo & Karjaluoto, 2007). Consumers thus perceive online shopping environments as carrying greater risk than offline retail stores (Lee & Tan, 2003).
Financial risk refers to the possibility of losing money and privacy risk refers to personal data becoming known or misused (Zhao et al., 2008). Previous studies have indicated that diurnal preferences are related to financial risk taking (Wang & Chartrand, 2015). As online monetary transactions inevitably involve the exchange of personal data (Liao, Liu, & Chen, 2011), both financial and privacy risks are taken into consideration in the present study. Given that perceived risks negatively influence intention to use (Chen & Chang, 2005; Faqih, 2011, 2013; Park, Lennon, & Stoel, 2005; Vijayasarathy & Jones, 2000), satisfaction with (Liu et al., 2008), and attitudes toward online shopping (Wu & Ke, 2015), we hypothesized that
H1: Perceived risk (financial and privacy risks) is negatively associated with satisfaction with online shopping.
H2: Perceived risk (financial and privacy risks) is negatively associated with attitudes toward online shopping.
H3: Perceived risk (financial and privacy risks) is negatively associated with intention to use online shopping.
Diurnal Preferences, Satisfaction with, Attitudes toward, and Intention to Use Online Shopping
Individuals with earlier sleep and wake times normally feel more satisfied with (Randler, 2008b; Jankowski, 2012; Díaz-Morales, Jankowski, Vollmer, & Randler, 2013) and display a more positive attitude toward life (Randler, 2011). They also perceive to have more consistent and healthier lifestyles while also tending to experience less psychological disturbances (Monk, Buysse, Potts, DeGrazia, & Kupfer, 2004; Randler, 2008b). Consumers consider online shopping a healthier lifestyle choice (Kim & Davis, 2009; Swinyard & Smith, 2003), which is more congruent with the self-concepts of individuals sleeping and waking earlier than others. Thus, individuals with earlier sleep and wake times are expected to experience higher levels of self-congruence than individuals with later sleep and wake times based on the self-congruency theory (Sirgy, 1982; 1986). This, in turn, enhances their satisfaction with, positive attitudes toward, and the use of online shopping. Hence, it was hypothesized that:
H4: Morningness-eveningness is positively associated with satisfaction with online shopping
H5: Morningness-eveningness is positively associated with attitudes toward online shopping
H6: Morningness-eveningness is positively associated with intention to use online shopping
Diurnal Preferences and Perceived Risk
Compared with individuals with later sleep and wake times, individuals with earlier sleep and wake times have been found to consume fewer harmful products, such as cigarettes (Wittmann, Dinich, Merrow, & Roenneberg, 2006) and psychoactive drugs (Fleig & Randler, 2009). They have also been found to be low in novelty-seeking, less impulsive (Caci, Robert, & Boyer, 2004), and less willing to take financial risks (Wang & Chartrand, 2015). Individuals with earlier sleep and wake times are therefore less predisposed to risk-taking. Based on self-congruency theory (Sirgy, 1982; 1986), individuals with earlier sleep and wake times are expected to experience lower levels of self-congruence/higher levels of self-incongruence when encountering perceived higher risk. Consequently, satisfaction with, positive attitudes toward, and intention to use online shopping in individuals with earlier sleep and wake times are expected to drop faster than in individuals with later sleep and wake times when perceived risk is increased. Individuals with earlier sleep and wake times are therefore more sensitive to perceived risk. Accordingly, it was hypothesized that Figure 1:
Figure 1: The Conceptual Model Dotted Lines (H7-9) Represent The Moderating Effects Hypothesis Is Abbreviated As H.
H7: Morningness-eveningness significantly moderates the relationship between perceived risk (financial and privacy risks) and satisfaction with online shopping
H8: Morningness-eveningness significantly moderates the relationship between perceived risk (financial and privacy risks) and attitudes toward online shopping
H9: Morningness-eveningness significantly moderates the relationship between perceived risk (financial and privacy risks) and intention to use online shopping
Snowball sampling was used to collect the data. Students from two higher education institutions in India were invited to complete the online questionnaires via email and to forward them to other college students, who could then forward them to other students. This cycle was repeated until sufficient data were collected. All participants were over 18 years of age and had shopped online within the past 12 months. Two university scholars and five Indian college students evaluated the face validity of the questionnaire prior to its administration. All participation was voluntary and resulted in 463 completed questionnaires; after the data were cleaned, 410 completed questionnaires were retained. Five- and four-point ordinal scales were used to measure morningness- eveningness, while seven-point Likert scales (1 - strongly disagree, 7 - strongly agree) were used to measure perceived risk as well as satisfaction with, attitudes toward, and intention to use online shopping.
Morningness-eveningness. The morningness-eveningness scale was adapted from Terman, Rifkin, Jacobs, and White (2001), which was itself adapted from a 19-item measurement designed by Horne and Östberg (1976). Its purpose was to measure circadian rhythms Total scores ranged from 16 to 86; higher scores indicated a morningness tendency and lower scores indicated an eveningness tendency.
Perceived risk (financial and privacy risks).To measure perceived risk (financial and privacy risks), five items adapted from Zhao et al. (2008) were used to assess uncertainty toward and the perceived consequences of online shopping.
Satisfaction with online shopping. To measure satisfaction with online shopping, a six- item assessment adapted from Zhao et al. (2008) was used to determine overall satisfaction with online shopping.
Attitudes toward online shopping. To measure attitudes toward online shopping, items adapted from Dabholkar and Bagozzi (2002) were used to record good/bad, pleasant/unpleasant, harmful/beneficial, and favorable/unfavorable feelings toward online shopping.
Intention to use online shopping. To measure intention to use online shopping, items adapted from Dabholkar and Bagozzi (2002) were implemented to determine the likelihood or unlikelihood of an individual continuing to use online shopping in the future.
Data collected consisted of 48% male respondents, 49.8% female, and 2.2% gender not specified; completed questionnaires from respondents aged 18 to over 40 (6.3% under 20, 75.6% 21-30, 11.7% 31-40, 5.3% over 40 years old) were used for analysis. The missing value for each variable ranged from 0.5% to 2.9%; thus, mean substitution was considered an efficient procedure for replacing the missing values (< 5%) (Rubin, Witkiewitz, Andre, & Reilly, 2007).
The morningness-eveningness scale was initially refined using Principle Axis Factoring (PAF). The four factors that emerged accounted for 46.56% of the total variance with eigenvalues of 3.175, 2.92, 1.63, and 1.12. Two factors were interpretable: eight items loaded on Factor 1 with factor loadings from .51 to .63 and six items loaded on Factor 2 with factor loadings from .52 to .63. After being analyzed using confirmatory factor analysis (CFA), Factors 1 and 2 yielded composite reliabilities (CR) of .73 and .65, respectively. Thus, Factor 2 had to be dropped due to low reliability (< .70) (DeVellis, 2016). The factor loadings for Factor 1 (eight items) ranged from .36 to.59 (p <.01).
The perceived risk scale (financial and privacy risks) was refined using Exploratory Factor Analysis (EFA) combined using Oblimin. All five items heavily loaded on a factor (factor loadings .87-.93) accounted for 80.82% of the total variance with an eigenvalue of 4.04. CFA was then conducted. All five items heavily loaded (factor loadings .82-.92) on a single abstract construct with t-values ranging from 21.72 to 23.90 (p <.01). Therefore, financial and privacy risks had to be combined into a single factor named financial and privacy risk to achieve composite reliability (.94).
Scales of satisfaction with, attitudes toward, and intention to use online shopping were refined using CFA. Six items heavily loaded on satisfaction with online shopping (factor loading .84-.90, p <.01), resulting in a composite reliability of .95; four items heavily loaded on attitudes toward online shopping (factor loadings .87-.91, p <.01), resulting in a composite reliability of .94; and four items heavily loaded (factor loadings .85-.89, p <.01) on intention to use online shopping, resulting in a composite reliability of .93. Table 1 shows the correlation matrix of constructs.
Correlation Matrix(N = 410)
|1. Perceived Risk ( Financial and Privacy)||1.00|
|2. Satisfaction with Online Shopping||-.29**||1.00|
|3. Attitudes Toward Online Shopping||-.39**||.84**||1.00|
|4. Intention to Use Online Shopping||-.37**||.81**||.87**||1.00|
|Notes: Sample size =410, **p<.01, *p<.05|
The hypotheses were then tested using hierarchical multiple regression analysis (Newsom, Prigerson, Schulz, & Reynolds, 2001). The independent variable and moderator were mean centered to avoid multicollinearity issues (Aiken, West, & Reno, 1991). The assumption of multicollinearity was examined using Variance Inflation Factor (VIF) analysis. VIF values from 1.23 to 5.33 were below the cutoff value of 10; therefore, the assumption of multicollinearity was accepted (Aiken, West, & Reno, 1991).
To test Hypotheses 1 through 3, two variables (financial and privacy risk and morningness-eveningness) were first entered into the multiple regression equation. The results, shown in Models 1, 3, and 5 of Table 2, indicated that financial and privacy risk was negatively associated with satisfaction with (β = -.11, p <.01), attitudes toward (β= -.15, p <.01), and intention to use online shopping (β= -.14, p <.01). Therefore, H1, H2, and H3 were supported.
The Moderating Effect Of Morningness Eveningness On The Relationships Between Perceived Risk And Satisfaction With, Attitudes Toward And Intention To Use Online Shopping
|Satisfaction with Online Shopping Attitudes toward Online Shopping Intention to Use Online Shopping|
| Independent Variables
|Model 1||t-value||Model 2||t-value||Model 3||t-value||Model 4||t-value||Model 5||t-value||Model 6||t-value|
|Financial & Privacy Risk (FPS) Moderator
|H1: -.11 *** H4: .64 ***||-2.65 8.25||-.12 *** .63 ***||-3.04 8.27||H2: -.15 H5: .63||*** ***||-4.51 9.84||-.16 *** .62 ***||-5.07 9.96||H3: -.14 H6: .51||*** ***||-4.48 8.53||-.15 *** .51 ***||-4.86 8.55|
|H7: -.03 ***||-3.49||H8: -.03 ***||-4.56||H9: -.02 ***||-3.34|
|F-value||56.31 ***||42.63 ***||93.06 ***||71.99 ***||75.24 ***||55.12 ***|
Moreover, as seen in Models 1, 3, and 5 of Table 2, morningness-eveningness was found to be positively associated with satisfaction with (β = .64, p <.01), attitudes toward (β= .63, p <.01), and intention to use online shopping (β = .51, p <.01). Therefore, H4, H5, and H6 were also supported.
Before testing the moderating effect of morningness-eveningness on the relationships among financial and privacy risk and satisfaction with, attitudes toward, and intention to use online shopping, financial and privacy risk and morningness-eveningness were included in hierarchical multiple regression analysis (see Models 1, 3, & 5 in Table 2). Financial and privacy risk and morningness-eveningness accounted for a significant amount of variance in satisfaction with online shopping, R2 = .22, F (2,407) = 56.31 (p< .01); attitudes toward online shopping, R2 = .31, F (2,407) = 93.06 (p < .01); and intention to use online shopping, R2 = .27, F (2,407) = 75.24 (p < .01). The interaction term between financial and privacy risk and morningness-eveningness was then added to the regression models (see Models 2, 4, & 6 in Table 2
), which accounted for a significant proportion of the variance in satisfaction with online shopping, Δ R2 = .02, ΔF (1, 406) = 12.18 (p = .01), b = -.03, t(406) = -3.49 (p < .01); attitudes toward online shopping, Δ R2 = .03, Δ F (1, 406) = 20.81 (p = .01), b = -.03, t(406) = -4.56 (p < .01); and intention to use online shopping, Δ R2 = .02, Δ F (1, 406) = 11.14 (p = .01), b = -.02, t(406) = -3.34 (p < .01). Thus, morningness-eveningness significantly moderated the relationships between financial and privacy risk and satisfaction with, attitudes toward, and intention to use online shopping. Therefore, H7, H8, and H9 were supported.
Further examination of the interaction plots (Figures 2, 3, & 4) suggested that satisfaction with, attitudes toward, and intention to use online shopping in consumers with earlier sleep and wake times (i.e., morningness) tended to decrease faster than in consumers with later sleep and wake times (i.e., intermediate or eveningness), whereas satisfaction with, attitudes toward, and intention to use online shopping in consumers with later sleep and wake times (i.e., eveningness) tended to decrease slower than in consumers with later sleep times (i.e., intermediate or morningness) due to the effect of financial and privacy risk.
Figure 2:Interaction Plot Of The Moderating Effect Of Morningness-Eveningness On The Relationship Between Perceived Risk (Financial And Privacy Risk) And Satisfaction With Online Shopping. Satisfaction With Online Shopping Is Abbreviated As Satisfaction, Financial And Privacy Risk Is Abbreviated As Risk.
Figure 3:Interaction Plot Of The Moderating Effect Of Morningness-Eveningness On The Relationship Between Perceived Risk (Financial And Privacy Risk) And Attitudes Toward Online Shopping. Attitudes Toward Online Shopping Is Abbreviated As Attitudes. Financial And Privacy Risk Is Abbreviated As Risk.
Figure 4:Interaction Plot Of The Moderating Effect Of Morningness-Eveningness On The Relationship Between Perceived Risk (Financial And Privacy Risk) And Intention To Use Online Shopping Intention To Use Online Shopping Is Abbreviated As Intention To Use. Financial And Privacy Risk Is Abbreviated As Risk.
In response to the growing importance of online shopping, the current study sought to understand the effect of diurnal preferences on online shopping. The results indicated that customers had less intention to use online shopping when they perceived higher financial and privacy risks, which confirmed predictions informed by the research of Chen and Chang (2005), Faqih (2011, 2013), Park, Lennon and Stoel (2005), and Vijayasarathy and Jones (2000). It was also found that customers formed more negative attitudes toward (Wu & Ke, 2015) and were less satisfied with (Liu et al., 2008) online shopping when they perceived higher financial and privacy risks.
Consistent with the findings of Díaz-Morales et al. (2013), Jankowski (2012), Kim and Davis (2009), Monk et al. (2004), Randler (2008b, 2011), Swinyard and Smith (2003), and self-congruency theory (Sirgy, 1982;1986); the results revealed that consumers with earlier sleep and wake times tended to feel more satisfied with, show more positive attitudes toward, and have higher intention to use online shopping. In contrast, consumers with later sleep and wake times tended to feel less satisfied with, show less positive attitudes toward, and have lower intention to use online shopping.
However, the results indicated that consumers with earlier sleep and wake times tended to be more sensitive to financial and privacy risks than consumers with later sleep and wake times, who tended to be less sensitive to financial and privacy risks. These findings were consistent with predictions based on the research of Fleig and Randler (2009), Wang and Chartrand (2015), Wittmann et al. (2006), and self-congruency theory (Sirgy, 1982;1986).
The current study contributes new knowledge to psychological and marketing research by employing self-congruency theory to link diurnal preferences with online shopping consumer intention. The current findings suggested that the relationship between perceived risk and online shopping intention is affected by circadian rhythms. Online shopping intention did not appear to be consistent across all hours of the day, but instead differed according to type of consumer and time of day. This phenomenon has implications for online retail managerial practices, which must take these behavioral patterns into account to formulate effective marketing strategies.
While 24/7 availability is often cited as a core advantage of online retail (Ebay, 2017), the question of how to target online consumers at different times of day has not yet been answered. The present work suggests that customers in the early morning and customers late at night are two different types of customers with different online shopping preferences and, by extension, different needs. Therefore, managers should use specific marketing strategies to meet these needs. When targeting customers in the early morning, for example, they may need to emphasize the reliability and trustworthiness of online payment systems to reduce perceived financial and privacy risks (Kim, Ferrin, & Rao, 2008; Kim, Tao, Shin, & Kim, 2010). In contrast, managers targeting customers with late sleep and wake times, who were found to be less sensitive to financial and privacy risk, may choose to focus on other aspects of online shopping that ensure customer satisfaction, such as customer service, delivery performance, information quality, merchandise attributes, product variety, reliability, transaction capability, and website design (Alam & Yasin, 2010; Liu et al., 2008; Lin & Sun, 2009; Luo, Ba, & Zhang, 2012).
In contrast to online customers in the early morning or late evening, online customers in the late morning and afternoon are more versatile and exhibit attributes common to other types of consumer (morningness, intermediate, and/or eveningness). Therefore, it may not be feasible to differentiate this type of customer. Online retailers may thus need to offer more personalized services when targeting these customers, such as allowing for the customization of products or services (Lee & Park, 2009).
To further enhance marketing efficiency and minimize business risk, online entrepreneurs can also launch start-up marketing campaigns that initially target consumers more likely to patronize again in the future (e.g., consumers with earlier sleep and wake times), as these customers feel satisfied and form positive attitudes more easily than other customers and are more likely to give repeat business to an online retailer. The campaigns could be gradually extended to consumers less likely to give repeat business to an online retailer (e.g., consumers with later sleep and wake times).
Although the present study can provide new and valuable insights regarding the effect of diurnal preferences on online shopping, it was not without limitations. Only Indian college students were recruited as participants; future research could be extended to other countries and include different groups of consumers. To further enhance the generalizability of findings, dimensions of perceived risk beyond financial and privacy risks could also be examined. Further, while the present research found different sensitivities to financial and privacy risks among consumers with early or late sleep and wake times, it was not designed to uncover the underlying mechanism(s) of such preferences. It was also not designed to determine how factors that determine circadian rhythms, such as light intensity or work schedule, affect the intention to use online shopping. Additional research is therefore needed to elucidate these factors and explore their effects on online shopping. Future research could also reveal the effect of diurnal preferences in different contexts (e.g., advertisements, products, services).
Aaker, J.L. (1999). The malleable self: The role of self-expression in persuasion. Journal of marketing research, 36(1), 45-57.
Achouri, M.A., & Bouslama, N. (2010). The Effect of the Congruence between Brand Personality and Self-Image on Consumers' Satisfaction and Loyalty: A Conceptual Framework. IBIMA Business Review.
Alam, S.S., & Yasin, N.M. (2010). An investigation into the antecedents of customer satisfaction of online shopping. Journal of Marketing Development and Competitiveness, 5(1), 71-78.
Amaro, S., & Duarte, P. (2014). Determinantes das intencoes de comprar viagens online: uma abordagem holistica. Revista Turismo & Desenvolvimento, 21, 115-117.
Andreassen, T.W. (2001). From disgust to delight: do customers hold a grudge? Journal of service research, 4(1), 39-49.
Belch, G.E., & Landon Jr, E.L. (1977). Discriminant validity of a product-anchored self-concept measure.
Beldad, A., De Jong, M., & Steehouder, M. (2010). How shall I trust the faceless and the intangible? A literature review on the antecedents of online trust. Computers in human behavior, 26(5), 857-869.
Belk, R. W. (1988). Possessions and the extended self. Journal of consumer research, 15(2), 139-168.
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS quarterly, 351-370.
Boswell, W. (2017). The Pros and Cons of Shopping Online.. Lifewire. August, 20, 2017.
Caci, H., Robert, P., & Boyer, P. (2004). Novelty seekers and impulsive subjects are low in morningness. European psychiatry, 19(2), 79-84.
Chen, K.J. (2005). Technology-based service and customers satisfaction in developing countries. International Journal of Management. 22(2), 307-318.
Chen, T.Y., & Chang, H.S. (2005). Reducing consumers’ perceived risk through banking service quality cues in Taiwan. Journal of Business and Psychology, 19(4), 521-540.
Chen, S.C., Chen, H.H., & Chen, M.F. (2009). Determinants of satisfaction and continuance intention towards self-service technologies. Industrial Management & Data Systems, 109(9), 1248-1263.
Cronin Jr, J.J., Brady, M.K., & Hult, G.T.M. (2000). Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments. Journal of Retailing, 76(2), 193-218.
Curran, J.M., Meuter, M.L., & Surprenant, C.F. (2003). Intentions to use self-service technologies: a confluence of multiple attitudes. Journal of Service Research, 5(3), 209- 224.
Czeisler, C.A., & Buxton, O.M. (2010). The human circadian timing system and sleep-wake regulation. In Principles and Practice of Sleep Medicine: Fifth Edition (402-419). Elsevier Inc.
Dabholkar, P.A., & Bagozzi, R.P. (2002). An attitudinal model of technology-based self-service: moderating effects of consumer traits and situational factors. Journal of the Academy of Marketing Science, 30(3), 184-201.
DeVellis, R.F., & Thorpe, C.T. (2021). Scale development: Theory and applications. Sage publications.
Díaz-Morales, J.F., Jankowski, K.S., Vollmer, C., & Randler, C. (2013). Morningness and life satisfaction: further evidence from Spain. Chronobiology International, 30(10), 1283- 1285.
Díaz-Morales, J.F., & Sánchez-Lopez, M.P. (2008). Morningness-eveningness and anxiety among adults: A matter of sex/gender?. Personality and Individual Differences, 44(6), 1391-1401.
Eastlick, M.A., Ratto, C., Lotz, S.L., & Mishra, A. (2012). Exploring antecedents of attitude toward co-producing a retail checkout service utilizing a self-service technology. The International Review of Retail, Distribution and Consumer Research, 22(4), 337-364.
Ebay. (2017). Advantages of Online Shopping and its Disadvantages. Retrieved February 10 2017.
Ekinci, Y., & Riley, M. (2003). An investigation of self-concept: actual and ideal self- congruence compared in the context of service evaluation. Journal of Retailing and Consumer Services, 10(4), 201-214.
Faqih, K..M. (2011, November). Integrating perceived risk and trust with technology acceptance model: An empirical assessment of customers' acceptance of online shopping in Jordan. In Research and Innovation in Information Systems (ICRIIS), 2011 International Conference on (1-5). IEEE.
Faqih, K.M. (2013). Exploring the influence of perceived risk and internet self-efficacy on consumer online shopping intentions: Perspective of technology acceptance model. International Management Review, 9(1), 67-77.
Fleig, D., & Randler, C. (2009). Association between chronotype and diet in adolescents based on food logs. Eating Behaviors, 10(2), 115-118.
Forehand, M.R., Deshpandé, R., & Reed II, A. (2002). Identity salience and the influence of differential activation of the social self-schema on advertising response. Journal of Applied psychology, 87(6), 1086.
Forsythe, S.M., & Shi, B. (2003). Consumer patronage and risk perceptions in Internet shopping. Journal of Business Research, 56(11), 867-875.
Frambach, R.T., Herk, H.V., & Agarwal, M.K. (2003). Culture's influence on Innovation adoption. A global study of managers' adoption intention of telecom innovations. Vrije Universiteit.
Goldstein, D., Hahn, C.S., Hasher, L., Wiprzycka, U.J., & Zelazo, P.D. (2007). Time of day, intellectual performance, and behavioral problems in morning versus evening type adolescents: Is there a synchrony effect? Personality and Individual Differences, 42(3), 431-440.
Graeff, T.R. (1996). Using promotional messages to manage the effects of brand and self-image on brand evaluations. Journal of Consumer Marketing, 13(3), 4-18.
Ha, S., & Im, H. (2012). Identifying the role of self-congruence on shopping behavior in the context of US shopping malls. Clothing and Textiles Research Journal, 30(2), 87-101.
Ho, S.Y., & Bodoff, D. (2014). The effects of Web personalization on user attitude and behavior: An integration of the elaboration likelihood model and consumer search theory. MIS Quarterly, 38(2), 497-A10.
Hogg, M.K., & Banister, E.N. (2001). Dislikes, distastes and the undesired self: conceptualising and exploring the role of the undesired end state in consumer experience. Journal of Marketing Management, 17(1-2), 73-104.
Horne, J.A., & Östberg, O. (1976). A self-assessment questionnaire to determine morningness- eveningness in human circadian rhythms. International Journal of Chronobiology.
Hsu, M.H., Yen, C.H., Chiu, C.M., & Chang, C.M. (2006). A longitudinal investigation of continued online shopping behavior: An extension of the theory of planned behavior. International Journal of Human-Computer Studies, 64(9), 889-904.
Images Retail Bureau. (2016). Online shopping trends: Facts & figures on Indian e-comm sector. Retrieved February 20 2017.
Jamal, A., & Al-Marri, M. (2007). Exploring the effect of self-image congruence and brand preference on satisfaction: the role of expertise. Journal of Marketing Management, 23(7- 8), 613-629.
Jankowski, K.S. (2012). Morningness/eveningness and satisfaction with life in a Polish sample. Chronobiology International, 29(6), 780-785.
Ju Rebecca Yen, H., & Gwinner, K.P. (2003). Internet retail customer loyalty: the mediating role of relational benefits. International Journal of Service Industry Management, 14(5), 483-500.
Kim, H.K., & Davis, K.E. (2009). Toward a comprehensive theory of problematic Internet use: Evaluating the role of self-esteem, anxiety, flow, and the self-rated importance of Internet activities. Computers in Human Behavior, 25(2), 490-500.
Kim, D.J., Ferrin, D.L. & Rao, H.R. (2008). A trust-based consumer decision-making model in electronic commerce: the role of trust, perceived risk, and their antecedents. Decision Support Systems, 44(2), 544-564.
Kim, M., & Lennon, S. (2008). The effects of visual and verbal information on attitudes and purchase intentions in internet shopping. Psychology & Marketing, 25(2), 146-178.
Kim, C., Tao, W., Shin, N., & Kim, K.S. (2010). An empirical study of customers’ perceptions of security and trust in e-payment systems. Electronic Commerce Research and Applications, 9(1), 84-95.
Ko, C.H., Hsiao, S., Liu, G.C., Yen, J.Y., Yang, M.J., et al. (2010). The characteristics of decision making, potential to take risks, and personality of college students with Internet addiction. Psychiatry Research, 175(1), 121-125.
Kressmann, F., Sirgy, M.J., Herrmann, A., Huber, F., Huber, S., et al. (2006). Direct and indirect effects of self-image congruence on brand loyalty. Journal of Business research, 59(9), 955-964.
Kuo, Y.F., & Wu, C.M. (2012). Satisfaction and post-purchase intentions with service recovery of online shopping websites: Perspectives on perceived justice and emotions. International Journal of Information Management, 32(2), 127-138.
Laroche, M., Yang, Z., McDougall, G.H., & Bergeron, J. (2005). Internet versus bricks-and- mortar retailers: An investigation into intangibility and its consequences. Journal of Retailing, 81(4), 251-267.
Lee, M.C. (2009). Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 8(3), 130-141.
Lee, E.J., & Park, J.K. (2009). Online service personalization for apparel shopping. Journal of Retailing and Consumer Services, 16(2), 83-91.
Lee, K.S., & Tan, S.J. (2003). E-retailing versus physical retailing: A theoretical model and empirical test of consumer choice. Journal of Business Research, 56(11), 877-885.
Lee, K., & Joshi, K. (2006, April). Development of an integrated model of customer satisfaction with online shopping. In Proceedings of the 2006 ACM SIGMIS CPR conference on computer personnel research: Forty four years of computer personnel research: achievements, challenges & the future (pp. 53-56). ACM.
Lee, M.Y., Kim, Y.K., & Fairhurst, A. (2009). Shopping value in online auctions: Their antecedents and outcomes. Journal of Retailing and Consumer Services, 16(1), 75-82.
Liao, C., Liu, C.C., & Chen, K. (2011). Examining the impact of privacy, trust and risk perceptions beyond monetary transactions: An integrated model. Electronic Commerce Research and Applications, 10(6), 702-715.
Livemint (2017). E-commerce market may cross $50 billion mark in 2018: report.
Lin, G.T., & Sun, C.C. (2009). Factors influencing satisfaction and loyalty in online shopping: an integrated model. Online Information Review, 33(3), 458-475.
Linville, P.W, Carlson, D.E. (1994). Social cognition of the self.
Liu, F., Li, J., Mizerski, D., & Soh, H. (2012). Self-congruity, brand attitude, and brand loyalty: a study on luxury brands. European Journal of Marketing, 46(7/8), 922-937.
Liu, X., He, M., Gao, F., & Xie, P. (2008). An empirical study of online shopping customer satisfaction in China: a holistic perspective. International Journal of Retail & Distribution Management, 36(11), 919-940.
Luo, J., Ba, S., & Zhang, H. (2012). The effectiveness of online shopping characteristics and well-designed websites on satisfaction. Mis Quarterly, 1131-1144.
Markus, H. (1977). Self-schemata and processing information about the self. Journal of personality and social psychology, 35(2), 63.
Marzocchi, G.L., & Zammit, A. (2006). Self-scanning technologies in retail: Determinants of adoption. The Service Industries Journal, 26(6), 651-669.
McEnally, M., & De Chernatony, L. (1999). The evolving nature of branding: Consumer and managerial considerations. Academy of Marketing Science Review, 2(1), 1-16.
Mitchell, V.W. (1999). Consumer perceived risk: Conceptualisations and models. European Journal of Marketing, 33(1/2), 163-195.
Mitchell, V.W., & Harris, G. (2005). The importance of consumers' perceived risk in retail strategy. European Journal of Marketing, 39(7/8), 821-837.
Mongrain, V., Lavoie, S., Selmaoui, B., Paquet, J., & Dumont, M. (2004). Phase relationships between sleep-wake cycle and underlying circadian rhythms in morningness-eveningness. Journal of biological rhythms, 19(3), 248-257.
Monk, T.H. (2005). Aging human circadian rhythms: Conventional wisdom may not always be right. Journal of Biological Rhythms, 20(4), 366-374.
Monk, T.H., Buysse, D.J., Potts, J.M., DeGrazia, J.M., & Kupfer, D.J. (2004). Morningness- eveningness and lifestyle regularity. Chronobiology International, 21(3), 435-443.
Mun, Y.Y., Yoon, J.J., Davis, J.M., & Lee, T. (2013). Untangling the antecedents of initial trust in Web-based health information: The roles of argument quality, source expertise, and user perceptions of information quality and risk. Decision support systems, 55(1), 284-295.
Nakade, M., Takeuchi, H., Kurotani, M., & Harada, T. (2009). Effects of meal habits and alcohol/cigarette consumption on morningness-eveningness preference and sleep habits by Japanese female students aged 18–29. Journal of physiological anthropology, 28(2), 83-90.
Neale, L., Robbie, R., & Martin, B. (2016). Gender identity and brand incongruence: When in doubt, pursue masculinity. Journal of Strategic Marketing, 24(5), 347-359.
Newsom, J.T., Prigerson, H.G., Schulz, R., & Reynolds III, C.F. (2001). Group differences in prediction in aging research: Statistical, methodological, and conceptual issues (pp. 1–45). Portland, OR: Portland State University.
O'Cass, A., & Grace, D. (2008). Understanding the role of retail store service in light of self?image–store image congruence. Psychology & Marketing, 25(6), 521-537.
Paine, S.J., Gander, P.H., & Travier, N. (2006). The epidemiology of morningness/eveningness: influence of age, gender, ethnicity, and socioeconomic factors in adults (30-49 years). Journal of Biological Rhythms, 21(1), 68-76.
Pappas, I.O., Kourouthanassis, P.E., Giannakos, M.N., & Chrissikopoulos, V. (2016). Explaining online shopping behavior with fsQCA: The role of cognitive and affective perceptions. Journal of Business Research, 69(2), 794-803.
Parasuraman, A., Zeithaml, V.A., & Berry, L.L. (1994). Reassessment of expectations as a comparison standard in measuring service quality: Implications for further research. The Journal of Marketing, 111-124.
Park, J., Lennon, S. J., & Stoel, L. (2005). On?line product presentation: Effects on mood, perceived risk, and purchase intention. Psychology & Marketing, 22(9), 695-719.
Pradhan, D., Duraipandian, I., & Sethi, D. (2016). Celebrity endorsement: How celebrity–brand– user personality congruence affects brand attitude and purchase intention. Journal of Marketing Communications, 22(5), 456-473.
Randler, C. (2008). Morningness?eveningness comparison in adolescents from different countries around the world. Chronobiology international, 25(6), 1017-1028.
Randler, C. (2008). Morningness–eveningness and satisfaction with life. Social Indicators Research, 86(2), 297-302.
Randler, C. (2011). Association between morningness–eveningness and mental and physical health in adolescents. Psychology, health & medicine, 16(1), 29-38.
Rubin, L.H., Witkiewitz, K., Andre, J.S., & Reilly, S. (2007). Methods for handling missing data in the behavioral neurosciences: Don’t throw the baby rat out with the bath water. Journal of Undergraduate Neuroscience Education, 5(2), A71.
Saleh, K. (2017). Global online retail spending – statistics and trends.
Salo, J., & Karjaluoto, H. (2007). A conceptual model of trust in the online environment. Online Information Review, 31(5), 604-621.
Schaal, S., Peter, M., & Randler, C. (2010). Morningness?eveningness and physical activity in adolescents. International Journal of Sport and Exercise Psychology, 8(2), 147-159.
Sirgy, M.J. (1982). Self-concept in consumer behavior: A critical review. Journal of Consumer Research, 9(3), 287-300.
Sirgy, M.J. (1986). Self-congruity: Toward a theory of personality and cybernetics. Praeger Publishers/Greenwood Publishing Group.
Sirgy, M.J., Grewal, D., Mangleburg, T.F., Park, J.O., Chon, K.S., Claiborne, C.B., ... & Berkman, H. (1997). Assessing the predictive validity of two methods of measuring self-image congruence. Journal of the academy of marketing science, 25(3), 229-241.
Soopramanien, D. (2011). Conflicting attitudes and scepticism towards online shopping: the role of experience. International Journal of Consumer Studies, 35(3), 338-347.
Susman, E.J., Dockray, S., Schiefelbein, V.L., Herwehe, S., Heaton, J.A., & Dorn, L.D. (2007). Morningness/eveningness, morning-to-afternoon cortisol ratio, and antisocial behavior problems during puberty. Developmental psychology, 43(4), 811.
Swann Jr, W. B., De La Ronde, C., & Hixon, J. G. (1994). Authenticity and positivity strivings in marriage and courtship. Journal of Personality and Social Psychology, 66(5), 857.
Swinyard, W.R., & Smith, S.M. (2003). Why people (don't) shop online: A lifestyle study of the internet consumer. Psychology & marketing, 20(7), 567-597.
Tajfel, H., & Turner, J.C. (2004). The social identity theory of intergroup behavior. In Political Psychology, 276-293.
Tepeci, M. (1999). Increasing brand loyalty in the hospitality industry. International Journal of Contemporary Hospitality Management, 11(5), 223-230.
Turek, F. W. (2000). Introduction to chronobiology: Sleep and the circadian clock. Principles and practices of sleep medicine, 319-320.
Urbán, R., Magyaródi, T., & Rigó, A. (2011). Morningness-eveningness, chronotypes and health-impairing behaviors in adolescents. Chronobiology International, 28(3), 238-247.
Vaidya, A. (2017). Online shopping trends among college students. International Journal of English Language, 5(8), 92-106.
Verhagen, T., & van Dolen, W. (2011). The influence of online store beliefs on consumer online impulse buying: A model and empirical application. Information & Management, 48(8), 320-327.
Vijayasarathy, L.R., & Jones, J.M. (2000). Print and Internet catalog shopping: assessing attitudes and intentions. Internet Research, 10(3), 191-202.
Wang, M. (2012). Determinants and consequences of consumer satisfaction with self-service technology in a retail setting. Managing Service Quality: An International Journal, 22(2), 128-144.
Wang, L., & Chartrand, T. L. (2015). Morningness–eveningness and risk taking. The Journal of Psychology, 149(4), 394-411.
Wittmann, M., Dinich, J., Merrow, M., & Roenneberg, T. (2006). Social jetlag: misalignment of biological and social time. Chronobiology international, 23(1-2), 497-509.
Wittmann, M., Paulus, M., & Roenneberg, T. (2010). Decreased psychological well-being in late ‘chronotypes’ is mediated by smoking and alcohol consumption. Substance Use & Misuse, 45(1-2), 15-30.
Wu, W.Y., & Ke, C.C. (2015). An online shopping behavior model integrating personality traits, perceived risk, and technology acceptance. Social Behavior and Personality: an International Journal, 43(1), 85-97.
Xu, H., Dinev, T., Smith, J., & Hart, P. (2011). Information privacy concerns: Linking individual perceptions with institutional privacy assurances. Journal of the Association for Information Systems, 12(12), 798.
Yang, Z., & Peterson, R. T. (2004). Customer perceived value, satisfaction, and loyalty: The role of switching costs. Psychology & Marketing, 21(10), 799-822.
Zhao, A.L.L., Stuart, H., Ward, P., & Goode, M.M.H. (2008). Perceived risk and Chinese consumers' internet banking service adoption. International Journal of Bank Marketing. 26(7), 505-525.
Zinkhan, G.M., & Hong, J.W. (1991). Self concept and advertising effectiveness: A conceptual model of congruency conspicuousness, and response mode. ACR North American Advances.
Zlomanczuk, P., & Schwartz, W.J. (1999). Cellular and molecular mechanisms of circadian rhythms in mammals. Lung biology in health and disease, 133, 309-342.
Received: 05-Dec-2022, Manuscript No. AMSJ-22-13015; Editor assigned: 06-Dec-2022, PreQC No. AMSJ-22-13015(PQ)); Reviewed: 20-Jan-2023, QC No. AMSJ-22-13015; Revised: 25-Feb-2023, Manuscript No. AMSJ-22-13015(R); Published: 09-Mar-2023