Role of impulsiveness in online purchase completion intentions: an empirical study among Indian customers

Rejikumar G. (Department of Management, Amrita Vishwa Vidyapeetham, AIMS Campus, Kochi, India)
Aswathy Asokan-Ajitha (Department of Management Studies, Indian Institute of Technology Madras, Chennai, India)

Journal of Indian Business Research

ISSN: 1755-4195

Article publication date: 2 November 2020

Issue publication date: 15 July 2021

2009

Abstract

Purpose

Online cart abandonment is a severe issue posing challenges to e-commerce growth. Emerging economies such as India fascinates global marketing practitioners because of favorable demographics and high levels of internet penetration. This study aims to consider the role of certain exogenous factors in developing shopping motivations that sequentially mediate to online purchase completion through impulsiveness under risk perceptions. The primary motivation behind this study is to understand the mental mechanism among online customers that develop purchase completion intentions, which prevent cart abandonment significantly.

Design/methodology/approach

Impact of e-commerce exogenous factors related to e-commerce such as website attributes, product features, promotional excellence and decision-making easiness on shopping motivations, impulsiveness and purchase completions intentions under the moderating effect of risk was estimated from the perceptions of Indian online customers (n = 243) using variance-based structural equation modeling and SPSS process macro v.3.0.

Findings

The most important exogenous variable that can influence purchase completion directly, sequentially through shopping motivations is decision easiness and promotions. Even though utility motivations are dominant in purchase completion intentions, hedonistic aspects are more critical in developing impulsiveness. The translation of impulsiveness to purchase completion is happening, but risk perception significantly moderates impulsiveness formation.

Research limitations/implications

Theoretically, this study examined online purchase completions being the most sought response by a customer to various stimuli in e-commerce. The study adopted a moderated mediation analysis in which shopping motivations and impulsiveness were mediators and risk as moderator. The interaction effect of risk on purchase completions was significant even when the mediating effects were prominent.

Practical implications

Contributes to the current knowledge-related online buying behavior in virtual retail formats and helps marketers in streamlining their focus in using impulsiveness as a strategic tool in reducing cart abandonment.

Originality/value

This study helps in understanding emerging trends in online buying behavior in India.

Keywords

Citation

G., R. and Asokan-Ajitha, A. (2021), "Role of impulsiveness in online purchase completion intentions: an empirical study among Indian customers", Journal of Indian Business Research, Vol. 13 No. 2, pp. 189-222. https://doi.org/10.1108/JIBR-04-2018-0132

Publisher

:

Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited


1. Introduction

Marketers, while designing their sales promotion campaigns, deploy many cues that develop impulsiveness among customers to engage in an unplanned buying. Impulse buying accounts for 59% of all purchases (Inman et al., 2009). Impulsivity is a trait that has a strong affective component and a lack of cognitive control over behavior. It is an irresistible urge that coincides with a lack of concern for objective reasoning (Bayley and Nancarrow, 1998). Customer characteristics such as age, gender, income level, education level, profession, marital status and ethnicity play an important role in impulsive purchases (Rook and Fisher, 1995). Many store attributes such as quality of salesperson interaction, the display attractiveness, promotion excellence and product attributes such as quality, packaging attractiveness and price benefits instigate unplanned purchases. Mostly, impulsive purchases offer gratification on the grounds of emotional aspects (Hoch and Loewenstein, 1991; Weun et al., 1998) that are more hedonic. The virtual retail formats offer rich experiences through technological innovations to develop impulsiveness. Mind-blowing displays, attractive shopping procedures, personalized customer care, etc. are few such attractions in e-commerce. A major challenge in e-commerce pertains to cart abandonment behavior of customers. Many times, the initial impulses created becomes insufficient to encourage purchase completions. The interest generated does not sustain until the end of the buying process. The global cart abandonment rate is as high as 70% (Baymard Institute, 2019) and is causing severe concerns to e-tailers. Future of e-commerce lies in controlling cart abandonment tendencies by embedding cues that develops impulsiveness that remains till purchase completions.

The extant literature focuses more on understanding the reasons behind cart abandonment (Egeln and Joseph, 2012; Kukar-Kinney and Close, 2010; Javadi et al., 2012; Xu and Huang, 2015) in a general perspective. The options suggested in such research studies to improve conversion rates were mostly through optimization of webscapes for better user experience. Therefore, web aesthetics, content, navigation easiness, display, shopping cart attributes, etc. were the topic of many studies. Further, the role of personal factors such as their efficacy, concerns, needs and choice conflict (Tversky and Shafir, 1992); psychographic factors such as variety seeking, price consciousness, impulsiveness, innovativeness and risk aversion; firm-specific factors such as image, credibility, trust and service quality; product-related factors variety, quality, discounts, packaging, etc.; factors causing task complexity in purchase completions such as process difficulty, technical delays and incomplete information supply were topic of many prior research on cart abandonment intentions in e-commerce. Despite the above, the problem of cart abandonment still prevails at a disturbing rate. The primary motivation behind this study is to understand the mental mechanism among online customers that develop purchase completion intentions, which can prevent cart abandonment significantly.

Literature confirms that impulsiveness in prevalent in online formats also (Chen et al., 2016; Zhang et al., 2006). Two perspectives of impulse buying like:

  1. An urge for purchase because of various stimuli from the shopping environment (Rook, 1987; Parboteeah et al., 2009); and

  2. An inherent personality trait that develops an urge for unplanned purchases (Rook and Fisher, 1995) exist.

Few studies have attempted to combine the perspectives of trait and environmental state (Wells et al., 2011). In another noteworthy attempt, Amos et al. (2014) suggested that three clusters of factors such as dispositional, situational and socio-demographic decide impulsivity. The dispositional factors are personality-related, situational are stimuli determined and socio-demographic factors refer to social and demographic aspects of the customer. The utilitarian and hedonic motivations develop impulsiveness leading to purchases in both offline and online (Rohm and Swaminathan, 2004; Hartman et al., 2006; Wang et al., 2006; Palazon and Delgado-Ballester, 2013; Lin et al., 2018). Evidence suggests that hedonic motivations can be non-purchase-oriented (Arnold and Reynolds, 2003; Yim et al., 2014) and may seek the only gratification by way of fun, pleasure or entertainment. Online formats are rich with hedonic aspects, and an over importance assigned to hedonism can significantly prevent purchase completions.

Extant literature is silent on the usage of impulsiveness stimulating cues on minimizing cart abandonment in e-commerce. Also, examining the role of hedonic and utilitarian motivations in imparting impulsiveness for minimizing cart abandonment is a less explored topic. In addition, the mammoth of evidence is from developed economies, where consumer behavior is much different from emerging economies such as India. At present, India is the most youthful society in the world and many drivers such as high internet penetration, emerging middle class, high disposable income among the young population and fast-growing retail market status, act in favor of online retail growth. Therefore, a contextual examination of consumer behavior about India is always contributing to scholars around the world. Thus, the purpose of this research is to strengthen the knowledge base in impulse-driven consumer behavior in the dynamic marketing environment in an emerging economy characterized by new consumer segments and innovative marketing strategies.

In this empirical study, we probe into the customer motivations related to hedonism and utility in e-commerce to identify its role in developing impulsiveness leading to purchase completion intentions among Indian customers. We presume that impulsiveness is a dominant mediator having the potential to prevent cart abandonment intentions of customers. A research model for the study is inclined to the stimulus-organism-response theory. Various factors such as the website, product, promotional excellence and other easy decision-making features related to e-commerce act as the stimulus and the response to such stimuli are purchase completion intentions. The impulsiveness developed in the organism (customer) mediates the formation of purchase completion intentions from hedonic and utilitarian motivations from stimuli. Thus, the effect of stimuli is likely to mediate through shopping motivations and impulsiveness (both referring to an organism) and develops responses such as purchase completion intentions. As documented in the literature, customers perceive many concerns related to privacy, security, etc. as risk in online purchases. Therefore, the impact of such risk perceptions can moderate the effect of changes in organisms on purchase completion intentions.

As virtual marketplaces will occupy major retailing in future, the paucity of research in the application of impulsiveness for positive outcomes prompted us to probe into the process by which impulsiveness in conjunction with customer shopping motivations, affects their purchase completion intentions in e-commerce. The key contribution of this research is estimating the strength of a few drivers of online shopping motivations and impulsiveness on shoppers’ decision to complete purchases under risk perceptions related to online. Consequently, the research offers implications for online retailers for reducing cart abandonment intentions of customers. The results of this research will help e-commerce firms and marketing professionals to devise strategies for addressing the discrepancy in the visit to sale ratio in e-commerce portals.

To conclude, this research aims to:

  • conduct a theoretical investigation about the mediating effects of e-commerce attributes on online purchase completion intentions through shopping motivations and impulsiveness among Indian customers;

  • a contextual investigation about the effect of selected e-commerce antecedents such as website aspects, product mix, promotional aspects and easy decision-making features on hedonic and utilitarian motivations of Indian customers; and

  • the moderating effect of perceived risk on shopping motivations to impulsiveness to purchase completion relationships among Indian customers.

2. Literature review

Impulsive buying is defined as “unplanned buying” (Stern, 1962) against the previous decisions on a purchase before the visit (West, 1951). The theoretical underpinnings behind the impulsive human behavior are available in the stimulus-organism-response framework (Woodworth, 1928). The theory postulate that individuals exhibit various mediating mechanisms to translate stimuli to behavioral responses. The mediating mechanisms are an individual’s cognitive and affective reactions that develop some behavioral outcomes, including purchase intentions (Russell and Mehrabian, 1976). Howard and Sheth (1969) model posits that consumer is rational, and the purchases decisions happen because of interaction between stimuli received from the marketing activities of the firm and the social environment. On receipt of the stimuli, perceptual and psychological processes occur in the customer mindset resulting in outcomes including purchase intentions mostly through a rational process. However, an individual’s response to stimuli can be irrational as proposed in the theory of bounded rationality (Simon, 1982) since constraints such as thinking capacity of individuals, available information and time limit rationality in decisions. Therefore, most people go for satisfactory decisions guided by impulsiveness rather than optimal. The immediate satisfaction on impulsive purchases corresponds to the expected utility perceptions in the expected utility theory (Von Neumann and Morgenstern, 1953).

Previous studies suggest that impulsive buying originates from the interaction of various external and internal stimuli that reduces rational thinking (Rook and Fisher, 1995; Liao et al., 2009; Kalla and Arora, 2011) capabilities of customers. The external stimuli are mostly marketplace-related and include aspects such as store environment and sales staff (Karbasivar and Yarahmadi, 2011; Mehta and Chugan, 2013); low prices and appealing promotional mechanisms (Virvilaite et al., 2009); attractive payment terms and conditions (Huang and Kuo, 2012). Similarly, internal stimuli are mainly customer centric factors related to personality and psychological aspects (Verplanken and Herabadi, 2001); level of amusement, delight, enthusiasm and joy (Weinberg and Gottwald, 1982); lifestyle traits linked to materialism and sensation seeking and recreational aspects of shopping (Rook, 1987). Most of the motives behind purchases are either rational (utilitarian) or emotional (hedonic) (Hirschman and Holbrook, 1982) and both contribute to value perceptions. Hedonic shopping motives pertain to emotion, comfort, joy, delight, adventurism, etc. perceived while on shopping whereas, utilitarian shopping motives represent the benefits accrued while shopping (Batra and Ahtola, 1991). In many impulsive buying decisions, hedonic motives override utilitarian motives. The retail outlet ambiance and atmosphere (Tendai and Crispen, 2009; Bridges and Florsheim, 2008) stimulates sensory perceptions and develop hedonic motivations (Beatty and Ferrell, 1998) by reducing consumer’s rationality in purchase decisions. Gender affects impulsive buying (Tifferet and Herstein, 2012) and female customers are more impulsive. Personality traits such as hedonic (easily tempted and enjoy spending), carelessness, cognitive and affective components in individual characteristics, lack of perseverance and lack of premeditation (disregard of the future) (Xiao and Nicholson, 2011) impart impulse purchases.

Both utilitarian and hedonic motivations have a significant role in online buying (Kukar-Kinney and Close, 2010). Utility perceptions in online emanate from multiple benefits such as convenience, information availability, variety product mix, cost and time efficiency (Katawetawaraks and Wang, 2011). Similarly, hedonic perceptions in online are from the possibility to experience fun, excitement, arousal, joy, festive, escapism, fantasy, adventure, etc. (Perea y Monsuwé et al., 2004). A wide variety of products displayed in an attractive manner in different categories and facilitated by easy navigational options makes shopping more enjoyable, and thus, impulsive. Promotional activities such as discounts, gifts, bonus offers and coupons enhance the quality of the shopping experience (Mishra et al., 2012). Online store layout also maximizes a convenience feel (Crawford and Melewar, 2003) and convert the shopping experience an exciting one.

2.1 Consequences of impulse buying

Every purchase decision is bound to produce cognitive dissonance when the customer observes a discrepancy between the expected and received levels of satisfaction (Festinger, 1957). The literature claims that cognitive dissonance is higher among individuals, with high impulsivity (George and Yaoyuneyong, 2010). Impulse buying occurs in the absence of self-control, and hence, adverse outcomes occur (Wood, 2005). The adverse consequences include financial problems, disappointment with the purchased products, feelings of regret and guilt and others’ disapproval (Mai et al., 2013). The positive feeling of joy and happiness is short-lived, and customers may face dissonance immediately. Under cognitive dissonance customer search for information to gain confidence and reduce dissonance (Sweeney et al., 2000). The ratification may be in consultation with friends or through further cost-benefit evaluations. In impulsive buying, the possibility of getting support from dissonance reducing cues are minimum as the satisfaction in the purchase is more related to the process rather than the product and customers sacrificing financial well-being for a short-term sense of emotional well-being.

2.2 Concluding remarks

The three-way elucidation of psychological experience by way of cognition (thinking), affect (feeling) and conation (behavior) portrays consumer behavior in response to various stimuli (Krugman, 1965; Haines et al., 1970; Smith and Swinyard). Marketing models mostly consider cognition as a pre-requisite for the behavioral response. Affect dominated paradigm of consumer response believes that cognitive evaluations are due to affect and results in conation (Zajonc and Hazel, 1982; Pieters and Van Raaij, 1988). A third view is that conation behavior is an outcome to stimuli and happens without much thinking or feelings (Nord and Peter, 1980). Impulsive buying behavior occurs in both cases where conation or affect overrides cognitions. The Stimulus-Organism-Response (S-O-R) framework postulate that physical and social stimuli in the environment influence the cognitive and emotional state of a person to engage in a behavior. Similarly, the flow theory postulates that the desire to repeatedly engage in an activity is directly related to the enjoyment element in the activity (Csikszentmihalyi, 1975). Evidence exists to believe that customers get higher enjoyment in online buying (Moon and Kim, 2001) and the flow of their experience has a profound influence on their impulsiveness. Also, when customers feel strong impulses, they exhibit more willingness to buy and under weak impulses, buying intentions significantly decline.

Amidst the presence of various favorable factors, many times, customers refuse to complete the purchase process online. The perceived transaction inconvenience associated with the checkout phase (Rajamma et al., 2009), security concerns, waiting for a sale or promotion, additional fees discovered upon the checkout, concerns over identity theft (Kukar-Kinney and Close, 2010), etc. are important reasons for abandoning purchase process. Variables related to uncertainty, website innovation factors and contextual factors in combination with certain consumer characteristics develop online shopping hesitation (Moore and Mathews, 2006) and moderate impulsiveness. Hence, an understanding about the role factors related to the website, product mix, promotional aspects, technology features for decision easiness and risk, etc. in concurrence with shopping motivations, transform impulsiveness to purchase completion intentions.

3. Hypotheses development

This study proposes to empirically examine the effect of certain e-commerce attributes on online customer’s hedonic/utilitarian motivations and impulsiveness leading to purchases completion intentions. With the advent of e-commerce, the concept of e-servicescape comprising of website factors, product offers, promotional aspects and other features that help easy decision-making emerged as key exogenous factors that influence consumer behavior. As postulated in the theory of reasoned action (Madden et al., 1992), a behavior follows the formation of intent in its favor. The intention is an outcome of an attitude formed from certain beliefs, and many external factors decide such beliefs. The external variables correspond to stimuli in the S-O-R paradigm, and intention refers to the response from the individual. The “organismic” reactions (Mehrabian and Russell, 1974) on receipt of stimuli will be emotional or cognitive that mediates to the response. Utility and hedonic motivations or the impulsiveness developed are such organismic changes having a mediating effect on stimuli to response formation. Literature suggests a gap between attitude to intention and intention to behavior in real time scenarios because of many contextual or social norm-related constructs (De Pelsmacker and Janssens, 2007; Carrington et al., 2010; Moraes et al., 2012). The concerns and associated inhibitions about online buying is, therefore, becoming a moderator in the organismic constructs to purchase completion intentions. The above observations guided the development of hypotheses in this study. Many hypotheses required empirical support to meet the objectives of this study. First, the direct effects from e-commerce attributes (exogenous variables) on shopping motivations; shopping motivations on impulsiveness; and impulsiveness on purchase completion intentions. Second, mediating effects of both shopping motivations and impulsiveness on purchase completion intentions. Third, the moderating effect of risk perceptions on mediating effects, if at all, developed because of shopping motivations and impulsiveness on purchase completion intentions.

3.1 Hypotheses related to various direct effects

In an online context, many exogenous factors contribute to customer shopping motivations (Tauber, 1972). The utilitarian shopping motivation reflects the usefulness, value and convenience perceived by the customer. In e-commerce, many aspects related to website such as usability (Shirmohammadi, Ebrahimi and Ghane, 2014); security (Nazir et al., 2012); information quality (Liao et al., 2009); aesthetic design and processing speed (Yoo and Donthu, 2001); trustworthiness of service provider (Chen and Dhillon, 2003); and online customer service (Wang, 2015), imparts utility perceptions. Intuitive interface design, clear navigation and appealing usable interface (Palmer, 2002) motivates self-service potential of the website and add utility perceptions to customers. E-commerce offers both place utility referring to anywhere shopping and time utility denoting anytime shopping (Rohm and Swaminathan, 2004).

Another important exogenous variable documented in literature as capable of developing utility perceptions is product offerings (Chiu et al., 2014). Online stores display a wide variety of products of different brands with detailed descriptions about specifications to impart higher utility and better shopping experience (Kim and Larose, 2004; Childers et al., 2001). Similarly, e-commerce portals use price discounts and related offers to promote products. Price promotions tend to positively influence customer estimates of the fair price to enhance value and satisfaction with a purchase (Darke and Dahl, 2003). Also, the economic benefit received from promotions imparts rationality to the purchase process, and thus, develops utility feel. Other promotional aspects such as free shipping, loyalty points, warranties and easy payment options to develop utilitarian motivations.

Every purchase decision is a complex process. Wrong decisions make customer’s skeptic and lead to the dissonance that significantly affects satisfaction from purchases. Virtual retail formats provide multiple options such as reviews, suggestions, comparisons and detailed product information (Lim and Dubinsky, 2004) for easy and better decision-making (Bhatnagar and Ghose, 2004) in minimum time. Customer reviews offering valid information about products (Lim and Dubinsky, 2004) reduces post-purchase dissonances. Category-wise search option (Szymanski and Hise, 2000) and comparison easiness reduces psychological costs (Prasad and Aryasri, 2009) and makes online shopping more cost and time efficient. In online shopping, convenience is about access, search, possession, transaction, time convenience and place. The virtual shopping cart offers (virtual) space to hold items for potential purchases and helps customers in organizing their purchases and facilitating research to obtain a better understanding of product attributes, and relative benefits in comparison with similar products. All the above aspects impart a feeling of decision easiness, and therefore, act as an exogenous variable developing utility feeling. The above observations are the rationale behind proposing the following hypotheses.

H1a.

Website attributes of e-commerce has a direct positive effect on utilitarian shopping motivations of Indian customers.

H1b.

Promotional features in e-commerce has a direct positive effect on utilitarian shopping motivations of Indian customers.

H1c.

Product attributes of e-commerce has a direct positive effect on utilitarian shopping motivations of Indian customers.

H1d.

Decision easiness perceived in e-commerce has a direct positive effect on utilitarian shopping motivations of Indian customers.

Similar to utilitarian motivations, hedonic motivations do play a significant role in consumer behavior. Hedonic motivation refers to those purchase behaviors in a quest for pleasure, fantasy, arousal and enjoyment (To et al., 2007). Personalization through user-friendly website designs provides navigation easiness to offer a better shopping experience and make shopping a memorable experience (Jones and Kim, 2010). Web layout and related aesthetics motivate customers to spend more time online, and such lengthy interaction imparts a feeling of relaxation and fun (Iyer, 1989). Product displays, its variety, descriptions and its presentation through images evoke hedonic motivations (Abdallah and Jaleel, 2015) from the pleasure of exploration in the shopping process (Westbrook and Black, 1985). In a similar manner, the concept of “value shopping,” (McGuire,1974) referring to shopping for sales, looking for discounts and hunting for bargains, offer increased sensory involvement and excitement (Babin et al., 1994), and therefore, such promotional features in online buying, develops hedonic motivations. The technology aspects in an online portal induce a sense of curiosity to users (Webster et al., 1993). The easiness in decision-making perceived from comparisons, review/suggestion analysis and categorized visualizations, helps customers to get an idea about new trends (Arnold and Reynolds, 2003) and significantly reduces stress (Arnold and Reynolds, 2003) attached to purchase decisions. The stress alienation leads to relaxation and better gratification, and thus, contributes to hedonic motivations. The following hypotheses are based on the above rationale.

H2a.

Website attributes of e-commerce has a direct positive effect on hedonic shopping motivations of Indian customers.

H2b.

Promotional features in e-commerce has a direct positive effect on hedonic shopping motivations of Indian customers.

H2c.

Product attributes of e-commerce has a direct positive effect on hedonic shopping motivations of Indian customers.

H2d.

Decision easiness perceived in e-commerce has a direct positive effect on hedonic shopping motivations of Indian customers.

Many studies have empirically established that both utility and hedonic motivations in online develop impulsiveness (Dawson and Kim, 2009; Wells et al., 2011; Verhagen and van Dolen, 2011; Park et al., 2012). A prominent view argues that emotions are predecessors of impulsiveness, and hence, hedonic motivations contribute more in impulsive buying (Yu and Bastin, 2010; Wolfinbarger and Gilly, 2001; Arnold and Reynolds, 2003; Zhou et al., 2007). However, many scholars commented that even utilitarian aspects equally contribute in impulsive behavior similar to hedonic motivations (Madhavaram and Laverie, 2004; Novak et al., 2003; Rezaei et al., 2016). The website acts as the place in the marketing mix of an e-commerce firm and many utilitarian features such as layout (Lindgaard et al., 2011), usability, (Fang and Holsapple, 2007), information content (Chiu et al., 2014) and pleasantness and appropriateness (Bonnardel et al., 2011) has the potential to evoke impulsiveness (Turkyilmaz et al., 2015). Hence, the estimation of such direct effects is useful. Therefore, we propose the following hypotheses

H3a.

Utilitarian motivations in e-commerce has a direct positive effect on the impulsiveness of Indian customers.

H3b.

Hedonic motivations in e-commerce has a direct positive effect on purchase completion intentions of Indian customers.

H3c.

Utilitarian motivations in e-commerce has a direct positive effect on the purchase completion intentions of Indian customers.

H3d.

Hedonic motivations in e-commerce has a direct positive effect on purchase completion intentions of Indian customers.

Impulsive customers experience a stronger urge than other customers to complete purchases (Gardner and Rook, 1988) and that internet shoppers are more impulsive than offline shoppers (Donthu and Garcia, 1999). The stages of the classical buying process involve problem recognition, information search, evaluation of alternatives, purchase and post-purchase dissonances. The impulsive buying tendency narrows done this process (Dholakia, 2000). Amidst, strong evidence about, impulsiveness to purchase intentions and that online has more cues for impulsiveness, cart abandonment remains as a puzzle. Therefore, contextual exploration of this linkage is a matter of significance to this study. Hence, we propose the following hypothesis.

H4.

Impulsiveness has a direct positive effect on purchases completion intentions of Indian online customers.

3.2 Hypotheses related to various mediating effects

Online customers perceive extra utility from quick and easy checkout, shorter or no queue, better control over the process (Berry et al., 2002), round the clock availability (Childers et al., 2001), absence of crowd and waiting lines, holiday shopping possibility, no need for face-to-face interaction with a salesperson (Park and Kim, 2008), option to do comparisons with similar products (Butler and Peppard, 1998), category-wise search option (Szymanski and Hise, 2000), etc. Similarly, better sensory experience from close-up pictures, zoom facility, two-dimensional or three-dimensional rotation, virtual try-on facility, etc. (Blum, 2016) offer easiness in decision-making, and thus, impulsiveness. Products offer the most powerful stimuli to provoke impulsiveness (Chen, 2008). Promotions are identified in many studies as reasons for impulsiveness, both in conventional and online formats (Dawson and Kim, 2009). Therefore, the indirect effect of e-commerce attributes on impulsiveness requires a contextual examination. It is, thus, proposed that

H5a.

Utilitarian motivations significantly mediates the effects of e-commerce attributes on impulsiveness among online Indian customers.

H5b.

Hedonic motivations significantly mediates the effects of e-commerce attributes on impulsiveness among online Indian customers.

Further, customers shopping goals (utilitarian/hedonic) influence their online shopping behaviors (Ha and Stoel, 2004; Schlosser, 2003). Online portal offers scope for both utilitarian and hedonic motivations. The cart feature in e-commerce help customers to organize the purchase and do extra research on their usefulness. When hedonic beliefs prompt depositing in the cart, a utilitarian perspective makes them evaluate the quality and value of the items. Thus, an interplay between both shopping motivations is likely in the last stage of the purchase. If utilitarian motivations override hedonic aspects, the likelihood of impulsiveness reduces (Strack and Deutsch, 2006) and the customers may leave the portal resulting in a cart abandonment (Paden and Stell, 2010). We assumed that the indirect effect of shopping motivations has a vital role in cart abandonment decisions, and hence, estimating the relative strength of indirect effects from shopping motivations to purchase completion intentions is important. Hence, the following hypotheses are proposed.

H6a.

Impulsiveness significantly mediates the effect of utilitarian motivations on purchase completion intentions among online Indian customers.

H6b.

Impulsiveness significantly mediates the effect of hedonic motivations on purchase completion intentions among online Indian customers.

Many studies have identified that e-commerce attributes develop purchase intentions; shopping motivations develop purchase intentions and impulsiveness develops purchase intentions. However, research on combined mediation effects of shopping motivations and impulsiveness on purchase completion intentions from exogenous variables (e-commerce attributes) is missing. Based on S-O-R theory, the organism mediates the formation of a response from stimuli, but the organism can perceive many feelings on receipt of stimuli. Hence, the formation of both value perceptions and urges are likely. The observation is valid as per tenets of the theory of reasoned action, that the two beliefs regarding shopping motivations and impulsiveness independently and jointly mediates the effect of e-commerce attributes to an intent to complete purchases online. Models related to such a “double mediation” are widely applied in many marketing studies (Jang et al., 2019; De Veirman et al., 2017; Hamilton et al., 2015). The above observation guided the development of H7a–H7d.

H7a.

The shopping motivations and impulsiveness together mediates the effect of website attributes of e-commerce on purchase completion intentions of online Indian customers.

H7b.

The shopping motivations and impulsiveness together mediates the effect of product attributes of e-commerce on purchase completion intentions of online Indian customers.

H7c.

The shopping motivations and impulsiveness together mediates the effect of promotional features in e-commerce on purchase completion intentions of online Indian customers.

H7d.

The shopping motivations and impulsiveness together mediates the effect of decision easiness in e-commerce on purchase completion intentions of online Indian customers.

3.3 Moderating effect of risk perceptions on mediating effects of shopping motivations and impulsiveness on purchase completion intentions online

Online buying has many uncertainties related to financial, performance linked, physical, psychological or social (Konana and Balasubramanian, 2005) and it can be fraudulent action from intermediaries (Adapa, 2008), inferior product performance (Hsin Chang and Wer Chen, 2008), privacy threat (van Noort et al., 2015) and time loss (Suresh and Shashikala, 2011). A customer will perceive risk when outcomes are unpredictable. Many studies identified that perceived risk is an important determinant of both purchases and repeat purchase intentions (Pavlou, 2003; Glover and Benbasat, 2010; Chiu et al., 2014). The prospect theory assumes that individuals make decisions based on estimated utility (Kahneman and Tversky, 2013) and under risk perceptions utility feeling reduces (Bornovalova et al., 2009). Similarly, under risk perceptions, an element of anxiety about outcome develops in the minds of the customer and reduces the overall hedonic feeling (Bhatnagar and Ghose, 2004). Literature suggests that when risk is more, the customer becomes cautious and engage in a better cognitive process and may control impulses (Lee and Yi, 2008). Thus, the following hypotheses.

H8a.

Risk perceptions of customer significantly moderates the mediating effect of impulsiveness from utilitarian motivations of Indian customers online.

H8b.

Risk perceptions of customer significantly moderates the mediating effect of impulsiveness from hedonic motivations of Indian customers online.

4. Research methodology

4.1 Research model

The research model, as depicted in Figure 1, represents all the hypotheses developed for the study. The model explains the formation of an intent to complete online purchases from antecedents related to online stimuli such as product, place(website), promotion, decision easiness(convenience), sequentially mediated through shopping motivations and impulsiveness. The model also examines the possible moderation in the outcome due to prevailing risk factors in e-commerce.

4.2 Measurement development

The literature review and discussions with experts helped to decide constructs and measures used. The operational definition of constructs was following contextual aspects and the selection of appropriate measures done after examining the contextual relevance of them in explaining the construct. The definitions are provided in Table 1.

The indicators to measure constructs were adapted from existing literature, as provided in Table 1. The measurement model specification requires a comprehensive understanding of the direction of the relationship between the measures and the latent construct to rule out the chances of model misspecification (Jarvis et al., 2003). Two possibilities of the direction of the relationship between measures and constructs exist as

  1. from the construct to the measure or

  2. from the measure to the construct.

The first one is reflective where, the measures are caused by construct and the second one is the case is formative, where the formation of the construct is from measures (MacKenzie et al., 2005). Here, measures represent are quantifiable scores collected through observation or survey about an item or indicator relevant in the measurement of the concept (Edwards and Bagozzi, 2000). Researchers suggest that for reflective constructs, correlations between items should be high and removal of an item does not change the essential nature of the underlying construct (Chin, 1998). Similarly, for formative constructs, item correlations are “not expected” and removing one item can later the predictive power of the construct.

Another associated concept in measurement theory is a decision on conceptualizing as unidimensional or multidimensional constructs. Multidimensional constructs have more than one dimension, and each dimension captures different facets that represents some portion of the overall latent construct under measurement (Law and Wong, 1999). Formative constructs are simplified multidimensional constructs (Petter et al., 2007), where a formative measurement item measure each sub-dimension of the construct and absence of multicollinearity is essential to rule out the measurement of the same aspects. Thus, one formative measurement item is enough for each dimension of a multidimensional construct (MacKenzie et al., 2005). In this study, the items identified were treated as formative, as each of them captures different facets of the concept and correlations among items were not essential on theoretical grounds. Hence, we treated all constructs as formative for estimating the parameters. We phrased the items mentioned in Table 1 into statements for collecting customer responses. The final statements were content validated with experts and were pilot tested and modifications made to remove all ambiguities in this regard.

Perceptions of online customers were sought using a structured questionnaire. The questions were close-ended statements illustrating the theme synonymous with indicators, and respondents had to cast their responses on a five-point Likert scale, varying from strongly disagree to strongly agree. The questionnaire was prepared in English, and contained a detailed description of the constructs along with the questions related to each construct, to minimize the extent of bias in the responses. The questionnaire contained questions on demographic characteristics such as gender, age, location and frequency of e-commerce website visits and questions to estimate the rate of cart abandonment and possible reasons for abandonment. The instrument was content validated through an expert review and corrections incorporated accordingly.

4.3 Data collection procedure

We opted an online survey for achieving maximum reach in a limited time. The online survey offers freedom to respondents for casting their views leisurely at their convenience, and hence, better involvement. Customers who are active in social media constituted the sample for the study. We have forwarded the link containing the online questionnaire to email address of potential respondents collected from social media blogs and e-commerce reviews. Besides, we have pasted the link in social media walls of Facebook, LinkedIn and few networking sites. We have forwarded the link to 367 respondents and have placed the link on social media for 30 days. We have sent reminders to those who have not responded in due intervals to ensure a better response rate. As the link was available to many respondents, a precise estimation of the response rate was not possible. However, we considered the procedure as adequate since, a consensus on acceptable response rate does not exist (Baruch and Holtom, 2008) and no clear evidence of a linkage between response rate and non-response bias exist (Groves, 2006). We could achieve a sample size of 243 during this period. In survey designs, possibility bias is minimum, if the sample selection is in a random manner (Draugalis and Plaza, 2009). In this study, samples selected are by pure chance, and no prejudice decided the selection process. However, to rule out the possibility of sampling bias, we opted for a statistical examination of randomness. First, we verified the normality of the data and found that absolute skewness and kurtosis values were less than 5 and 10 to rule out significant deviations from normality (Chin, 1998). Second, verified with the “runs” test (Bradley, 1968), about the randomness in the sample. Third, one of the major assumptions in the cause-effect investigation is about the independence of observations for ruling out the possibility of relatedness (autocorrelation). The Durbin-Watson statistic should be between 1.5 and 2.5 (Hair et al., 1998) to rule out autocorrelation, and thus, data independence. In this data, the Durbin-Watson statistic was 1.96. The results justified statistical estimation using parametric procedures.

The sample contained about 54.3% of men. About 33.2% were between 18yrs and 28yrs, 28.7% were between 29yrs to 38yrs, 18.4% were 38yrs to 47yrs and remaining above 47yrs. A majority (74%) of respondents reported the frequency of visiting online stores at least once a month, and about 52% reported they abandon carts more frequently. The most important reason for non-completion of purchases was the technical reasons (42%) followed by confusion in the completion processes (36%).

5. Data analysis

The structural equation modeling (SEM) approach helped in assessing the intensity of the relationship among variables. SEM estimates:

  • measurement model that explains how indicators come together to measure constructs and

  • structural model that represents how constructs are related to each other.

There exist two complementary versions of SEM, namely, covariance-based SEM and variance-based partial least square (PLS) SEM. PLS-SEM is more accepted when the research objective focuses on prediction and explaining the variance of key target constructs by different explanatory constructs and produces more reliable results when the sample size is relatively small, and the data has a deviation from normality (Hair et al., 2012). The covariance-based SEM is more focused on theory testing and confirmation (Wu and Li, 2018). As the aim of this study was for estimating the strength of relationships among hypothesized linkages, and, as all the constructs used in the study were assumed as formative, we estimated the model using the PLS-based software. Realizing the possibility of nonlinear relationships on verification of bi-variate plots among few latent variables (Guo et al., 2011), we chose WarpPLS software (version 6.0) to analyze the data because of its capability to test both linear and nonlinear relationships (e.g. U-shaped and S-shaped functions) in an integrative manner (Kock, 2011). Warp PLS 6.0 has different estimation algorithms for measurement models and structural model. Many relationships among behavioral variables are nonlinear (Kock and Gaskins, 2016) and “warp” algorithms attempt to identify such relationships. The software has multiple inbuilt re-sampling methods to estimate p-values. We opted:

  • “PLS regression” for measurement model in which weights are calculated through a least squares regression as an exact linear combination of the indicator scores (Kock and Mayfield, 2015).

  • “Warp2” for structural model to identify nonlinear relationships.

  • Bootstrapping re-sampling method that maximizes the variance explained by the latent variable indicators (Kock, 2014) for estimation. Further, we have controlled the effect of age and gender as proposed by Kock (2011).

5.1 Measurement model

The measurement model explains how each set of items relates to its construct or latent variable. The criteria related to reliability, convergent validity and discriminant validity helps in detecting adequacy of measurement models (Fornell and Larcker, 1981). Convergent validity is achieved if all the indicator weights are significant at p < 0.05, and the average variance extracted (AVE) should be greater than 0.50 (Kock, 2014). Furthermore, discriminant validity is verified by the difference between the AVE of a construct and its correlations with other constructs. To achieve sufficient discriminant validity, the square root of AVE of a construct should be higher than its correlations with all other constructs. Table 2 provides the details of variable correlations and AVE for establishing discriminant analysis.

To confirm the reliability of formative indicators; the variance inflation factor (VIF) for all constructs should be less than 3.3. Also, full collinearity VIFs verify for common method bias, and we got full collinearity VIFs of all constructs lower than 3.3 to confirm that the model is free from common method bias (Kock, 2014). On estimation, all formative items except one item, namely, “excitement” in the hedonic construct were significant with p < 0.05. Hence, this item was removed from further analysis. Subsequent estimation confirmed the meeting of all major quality criteria and the fitness of the model for hypotheses testing. The relevant estimates are reported in Table 3.

5.2 Structural model

The model fit criteria and estimates reported in the Warp PLS output, provide information on the validity and reliability of the model to conclude on causal assumptions made. To assess model fit, indices used are average path coefficient (APC), average R-squared (ARS), average adjusted R-squared (AARS), average block variance inflation factor (AVIF) and average full collinearity VIF (AFVIF). APC, ARS and AARS should have P-values equal to or lower than 0.05 while AVIF and AFVIF values should be equal to or lower than 3.3, for models measured using multiple indicators (Kock, 2014). The goodness of fit (GoF) for the model was above the recommended value for a large fit (Tenenhaus GoF = 0.589). The above observations confirmed the validity of the model to verify relationships hypothesized. R2 values of the dependent variables represent the predictive power of the theoretical model, and standardized path coefficients explain the strength of the relationship between the variables (Gefen et al., 2011). The estimated model reported R2 values more than 0.5 for all dependent variables confirming that the independent variables in the model explain substantial amount variance.

5.3 Results of hypotheses tested

The first four sets of hypotheses covered under H1 to H4 were about direct effects. The SEM estimates established significant direct effects to support all the hypotheses except H1c, which got support only at 0.1 level. Table 4 provides the results of direct effect-related hypotheses.

Estimation of multiple mediation models offered support for mediation hypotheses. First, the simple mediation effect of each type of shopping motivation on exogenous variables to impulsiveness (H5a–H5b). Second, mediation effect of impulsiveness on shopping motivations to purchase completion (H6a–H6b). Third, the serial mediation effect of shopping motivations and impulsiveness on exogenous variables to purchase completions (H7a–H7d). Apart from the above hypothesized mediation effects, we also examined the parallel mediation effect of hedonic and utilitarian motivations together on exogenous variables to impulsiveness. For mediation/moderation analysis, we used the process v3.0, developed by Hayes (2017), which is more robust in handling complex mediating/moderating models (Hayes, 2017). For testing simple mediation and parallel mediation (H5a–H5b, H6a–H6b), we used the Models 4 and 6 for the serial mediation models (H7a–H7d). To test simple mediation, we relied upon the estimate of indirect effects, which is less prone to error (Kock, 2014), as well as the lower bound and upper bound bootstrap confidence interval (95%). To consider an indirect effect to be significant the confidence intervals do not include zero and should be entirely below or above zero (Kane and Ashbaugh, 2017). For a significant mediating effect, the indirect effect must be significant and the P-values associated all the three paths coefficients must also be significant on a one to one basis. If the conditions met, and the P-value associated with the direct effect is not significant, then full mediation results and when P-value associated with the direct effect is still significant but with less quantity, partial mediation results (Kock, 2014). The relevant output of SPSS process Macro estimation is provided in Table 5. An examination of lower bound and upper bound confidence intervals revealed that all indirect effects except one representing web attributes to hedonic to impulsiveness do not include zero, and hence, significant. As in all cases, the direct effects, remained significant with mediator to conclude partial mediation. Thus, H5a–H5b got support except that H5a got only partial support, as the mediating effect of hedonic motivations on web attributes to impulsiveness got rejected.

Analysis of parallel mediation through both hedonic and utilitarian motivations revealed that the total indirect effect of all exogenous variables on impulsiveness are significant. However, the direct effects with mediator were significant to support partial mediation. The maximum parallel mediation effect was on decision easiness to impulsiveness (estimate = 0.405, 95% CI [0.268, 0.537]) followed by product attributes to impulsiveness (estimate = 0.367, 95% CI [0.219, 0.519]). Similarly, analysis of the indirect effect of hedonic motivations and utilitarian motivations on purchase completion intentions mediated through impulsiveness was done by comparing direct, indirect, and total effects. Table 6 provides details of effects comparison. H6a and H6b got support, as the indirect effects were significant and that impulsiveness mediates hedonic motivation with a higher indirect effect than utilitarian motivations.

The effect of exogenous variables on purchase completion intentions can occur through three routes:

  • mediated through shopping motivations and impulsiveness;

  • mediated through shopping motivations alone; and

  • mediated through impulsiveness alone.

H7a–H7d relate to these scenarios. The Model 6 illustrate these cases. Four exogenous variables formed “X” variables and the purchase completion intentions were “Y” variable and the mediators were shopping motivations and impulsiveness. Table 7 reports the outcomes of eight cases of serial mediation analysis. Examination of 95% CI confirms that except web attributes, all other exogenous variables mediated through hedonic motivations and impulsiveness since had significant indirect effects confirming serial mediation. Thus, H7a–H7d got support except for one part of H7a referring to hedonic mediation.

In H8a–H8b, we examined the moderating effect of risk on the indirect effect between shopping motivations (utilitarian/hedonic) and completion intentions via impulsiveness (Figure 2).

To test the moderated mediation hypothesis, we used estimates of three regression models illustrated in model 59 of PROCESS macro. The estimates included the moderating effect of risk on:

  • the relationship between utilitarian(hedonic) to purchase completions;

  • the relationship between utilitarian(hedonic) to impulsiveness; and

  • the relationship between impulsiveness and purchase competitions.

Moderated mediation exists if either or both of the mediated paths are moderated by risk (Hayes, 2017). In the case H8a, there was a significant interaction effect of risk and utilitarian motivations on impulsiveness (estimate = −0.171, p < 0.05) and a significant interaction effect of risk and utilitarian motivations on purchase completions (estimate = −0.132, p < 0.05). The interactive effect of risk and impulsiveness was also significant (estimate = −0.143, p < 0.05) on completion intentions. These results offer support to H8a. Similarly, in the case of H8b, there was a significant interaction effect of risk and hedonic motivations on impulsiveness (estimate = −0.256, p < 0.05), but no significant interaction effect of risk and hedonic motivations on purchase completions noticed. Hence, a first stage moderation existed and support to H8b. All significant moderating effects were negative in sign on a positive linkage, predicting that as risk perceptions increase from low to high, the power of shopping motivations to predict impulsiveness significantly reduces. We have plotted the effect of high, medium and low levels of risk on significant linkages. The plots are presented in Figure 3.

6. Conclusions and discussion

There are several conclusions from this study. First, impulsiveness is a dominant predictor of purchase completions (β = 0.392, p < 0.05), and risk moderate the power of impulsiveness in predicting purchase completions significantly. Hence, developing higher levels of impulsiveness under minimum risk perceptions can significantly control cart abandonment tendencies. We also found that risk perception significantly moderate causation of impulsiveness from both hedonic and utilitarian motivations. The strength and significance of moderation are more in the case of hedonic motivations. Therefore, when customers perceive utility, risk perceptions could exercise relatively less effect in cart abandonment decisions. These observations conclude that cart abandonment minimization using hedonic aspects is possible only when risk perceptions are minimum.

Second, utility perceptions are more dominant determinants of purchase completion intentions. In the direct effect model, the direct effect from utility (β = 0.412, p < 0.05) to purchase completions was much stronger than hedonic (β = 0.118, p < 0.05) to purchase completions. Also, impulsiveness is a significant mediator in the linkages from hedonic and utility motivations to completion intentions. We observed that due to mediating effect of impulsiveness the direct effect from hedonic to completion intentions reduced substantially (β = 0.37, p < 0.050.37 to β = 0.118, p < 0.05) compared to direct effect from utility to completion intentions (β = 0.46, p < 0.05 to β = 0.412, p < 0.05). Therefore, hedonic aspects need to be more impulsive for purchase completions.

Third, among the exogenous variables considered, website attributes evoked better utility (β = 0.301, p < 0.05) compared to hedonic (β = 0.277, p < 0.05). Many studies have established that aesthetics and visual elements in the website appeal to online customers and evoke their impulsiveness (Ganesh et al., 2010; Lin, 2007). Similarly, navigational easiness (Thielsch and Hirschfeld, 2012), visual aspects of the website (Kim and Niehm, 2009), etc. improve the credibility of online store and motivate customers. Among the web attributes, maximum loading was for navigational easiness followed by procedural simplicity. Absence of these attributes develops cart abandonment intentions. Browsing issues and complex procedures find a place in many reports as cart abandonment reasons (Baymard Institute, 2019). The processes in the checkout stages are more complex for many customers. Checkout experience optimization by revamping the website attributes may bring positive outcomes.

Fourth, promotional aspects develop higher hedonic feel (β = 0.281, p < 0.05) than utility (β = 0.245, p < 0.05). Promotional aspects have a significant total effect on impulsiveness and completion intentions. The findings of this study corroborate with previous findings (Liao et al., 2009; Dholakia, 2000; Puri, 1996). Promotions facilitate cognitive evaluations by assessing the cost-benefit equations in a purchase. Online marketplaces offer several sales promotions, such as gifts, discounts or free shipping, to attract customers. Promotion serves as an immediate economic incentive that develops impulsiveness. The customer believes that online prices are much lower than offline stores, therefore rather than price discounts, other forms of promotion are more suitable to impart sustainable impulses. In this study, promotions having nonfinancial implications such as loyalty points and easy payment options had the highest loading among all indicators used for measurement.

Fifth, the products develop more utility motivations (β = 0.161, p < 0.05) than hedonic (β = 0.126, p = 0.06). Products do develop a significant total effect on impulsiveness or completion intentions. The role of products in developing impulsiveness is established in both offline and online format (Ozen and Engizek, 2014; Moe, 2003). However, contextually, we found that products are evoking utility motivations, and are next to decision easiness in developing purchase completion intentions. In online, customers can see the products but cannot have the option to touch and feel. This significantly reduces the hedonic motivations, and thus, impulsiveness. The observation is valid among Indian customers whose buying process provides undue importance to the physical appreciation of quality. Minor variations in color in the case of apparels is a common complaint raised by online customers. Concerns about the mismatch from what is perceived in the virtual plane can significantly affect their value perceptions. The agreement expressed by customers in response to a question on “products delivered were not exactly matching with online” (mean = 3.838), underlines this observation. Therefore, visually appealing presentation of products using innovative technologies may impart better hedonic value to customers.

Sixth, the decision easiness develops more utility motivations (β = 0.242, p < 0.05) than hedonic (β = 0.207, p < 0.05). Decision easiness has a significant total effect on impulsiveness and significant total effect on completion intentions. Decision easiness develops a significant indirect effect on completion intentions. Online customers regard reviews and suggestions important in decision-making. Even though researchers have expressed their confusion on the authenticity of reviews (Hu et al., 2008), reviews motivate customers (Jo and Oh, 2011). However, the strategic use of reviews to develop impulsiveness needs further support. It is always good for firms to keep track of reviews appearing on the websites and engage in communicating with customers using such inputs. The absence of easiness in decision-making may impart cognitive complexity and can evoke risk avoidance behavior leading to cart abandonment.

Seventh, we found that all exogenous variables except web attributes develop purchase completion intentions either directly or mediated through motivations and impulsiveness at 0.05 level. In contributing to the serial mediation effect, only web attributes failed to get significance, but the shopping motivations and impulsiveness individually and together mediate the relationship between exogenous variables and purchase completion intentions. The significant indirect effect of shopping motivations and impulsiveness underlines the importance of psychological mechanisms of customers in minimizing cart abandonment. Finally, two of our hypotheses could not get support. The web attributes failed to had a significant indirect effect on impulsiveness mediated thorough hedonic motivations and on completion intentions. We assign the reasons for non-support to contextual factors related to Indian online customers. Indian customers are generally skeptic and cherish many beliefs in purchase decisions. The joint decision-making for purchases is still a dominant feature among Indian customers. Additionally, the development of trust perceptions about e-commerce among Indians is in the budding stage. Trust is an essential antecedent to impulsiveness (Whysall, 2000). Trust develops over a period. This study found that concerns about privacy loss, logistic performance and anxiety attached to online purchases are disturbing Indian customers. Such beliefs have moderated the indirect contribution of few exogenous variables in the development of impulsiveness and completion intentions. Thus, e-commerce firms must ensure customer confidence by introducing policies that safeguard their interests and ensure more safety and security in transactions

7. Implications and future research

7.1 Theoretical implications

Research on cart abandonment in the e-commerce is in the budding stage and attempts to address such challenges by managing the e-environment stimuli to develop impulsiveness compelling purchase completion intentions have theoretical implications. Research on theoretical explanations behind cart abandonment mostly examined the technological, webscape-related and customer-specific aspects. In this study, we attempted to examine the scope of developing a theory to predict purchase completion intentions online to control cart abandonment. We adopted the S-O-R theory as an overarching one to explore the online buying behavior of the customer. Accordingly, the proposed model of online purchase completion intentions included important e-commerce attributes that form the stimuli capable of evoking value perceptions leading to impulsiveness. Thus, this research theorized and tested the serial mediation effect of shopping motivations and impulsiveness together to capture the organism’s mental changes on receipt of stimuli. Further, we introduced risk perceptions, a widespread issue that critically intervene in online purchase completions as a moderator to examine the changes in the responses of the organism.

The insights from this study corroborate the propositions coined in the classic model of Mehrabian and Russell (1974) regarding the influence of shopping environment on the behavioral responses of a customer. The theoretical explorations about the relationship between exogenous attributes of online formats and customer’s cognitive evaluations in the backdrop of collectivist cultural setting contribute to the body of knowledge. The pleasure-arousal- dominance model (Mehrabian and Russell, 1974) postulates that positive perceptions about e-environment attributes develop certain emotional states in a customer to form an attitude in favor of adoption. Similarly, Stern (1962) suggests that impulsiveness in a conventional format emanates from the prevalence of certain factors such as low price, mass distribution, self-service, mass advertising and prominent store displays, which imparts a feeling of easiness. The empirical findings from this research confirm the role of both e- environment attributes and easiness imparting features of online in developing behavioral changes, and thus, validates the above paradigms. In every decision, an interplay of emotions and rationality occurs, and we could empirically establish through this research that the hedonic motivations representing the emotional stimuli, as well as utilitarian motivation expressing rational beliefs do significantly engage in interaction in predicting impulsiveness.

Our findings corroborate with the two-factor cost-benefit framework proposed by Puri (1996), that benefit perceptions above cost is a significant motivator of impulsiveness. We could confirm that utility perceptions have a higher role in online impulsiveness. Thus, the findings offer support to expected utility theory, which proposes the importance of utility motivation in significantly developing favorable outcomes. Similarly, our study could reconfirm the flow theory, that a continuation of an enjoyable activity is likely and, in e-commerce, emphasis on hedonic aspects can significantly impart purchase completion intentions. However, our findings suggest the occurrence of hesitation in purchase completion as documented in the conflicts–ambivalence–hesitation–abandonment framework proposed by Huang et al. (2018), as the risk perceptions had negative moderation potential. Thus, we could validate the contributions of Cho et al. (2006) about shopping hesitation on the internet.

This study reveals many salient linkages between stimulus and response, and thus, contribute to emerging body literature on online impulse buying. Many variables appearing in this study are used in the same or similar way in many other studies. However, the behavior of these variables among customers in the Indian context is less explored, and therefore, contributes to the literature on consumer behavior in emerging economies. We believe that generalizations of many findings from this study are possible because consumer behavior in many emerging economies have similarities. Also, the methodology in this study can help many researchers in emerging economies to plan similar studies aimed at addressing cart abandonment.

7.2 Managerial implications

Many reports suggest that cart abandonment is dissimilar in different stages of the buying process (for, e.g. internet Retailer report and Baymard institute). Taking insights from many such reports, 46.1% of cart abandonments happen at the payment stage, 37.4% occur at checkout login, 35.7% occur on disclosure of shipping costs, 20.9% occur when detailed information is sought for billing (Andrew, 2016). Lengthy and complicated checkout processes enhance cart abandonment (Chou et al., 2014; Shrivastava, 2014). Online customers’ get the motivation to purchase from both utilitarian and hedonic purposes, but from this study, we found that utilitarian feelings are best predictors of purchase completions. The features embedded in an e-cart enable the customers to organize the purchases more diligently based on their requirements. Many times, a later verification the customer decides to remove many items from the cart after subsequent evaluations of utility on purchase. The impulsiveness shown by putting items in cart, diminishes slowly. Hence, a quick checkout process can be useful. Further, additional offers for instant or speedy purchases such as “get 10% off if ordered within 2 min” may find beneficial.

This study confirms that a well-designed website facilitating fast decision-making evoke impulsiveness. Perhaps, the most significant implication to practitioners from this study is that impulsiveness is more contingent on external cues that are more objective, and hence, benchmarking needed to have clarity on ideal levels of these factors. We could find that among Indian customers’ web attributes alone fail to mediate to completions intentions. Hence, practitioners need to focus on quality and the feeling that products will appear similar in reality as displayed online. Because customers are not able to touch or try products before they buy, chances of post-purchase dissonance are more online. Service mechanisms such as a free trial, money-back guarantee (Comegys et al., 2009) and free service at the customer location can be considered to offer confidence to customers.

In addition, minimizing risk perceptions can significantly improve purchase completion intentions. Therefore, firms need to undertake a careful assessment of risk parameters in the process and should extend effective communication with customers in every phase of the process to offer better safety and security feelings. To improve the quality of service, e-form inquiry, order status tracking, timely customer feedback (Lim and Dubinsky, 2004) are useful. Post-purchase satisfaction is important in getting good reviews and in developing impulsiveness in subsequent encounters. Finally, firms can use insights from this research to leverage activities to attract and retain the Information Technology-savvy youth segment by creating customer relationship management applications that offer personalized offers and benefits. Further, in a besieged manner, e-commerce firms should adopt innovative, dynamic and relevant marketing mix components to offer customized solutions to customer needs.

7.3 Limitations and future research

This study purely focused on external cues in the control of firms in developing impulsiveness. The impact of non-controllable factors such as customer personality, psychographics, choice overload and information overload are not considered. This study included many relevant constructs, but e-cart features, season influences or brand priorities are not considered. Again, the social value perceptions are not included along with hedonic and utilitarian value perceptions in the theory proposed. The sample had representation from the entire country but was skewed toward urban customers. A larger and more representative sample might have helped in making comparisons across different categories in the population.

This study exposed many areas for potential research. The study was unveiled in the cultural context of India. The current purchasing decisions in India are in favor of group decision-making and peer acceptance. Indians prefer to buy a product after trials and demonstrations. The existing beliefs of enjoyment in the buying process need to be redefined to adopt e-commerce better. A clear understanding of the existing beliefs-related purchase process in e-commerce is critical to devising mechanisms that appeal to Indians. How cultural transformations going to affect online behavior is another exciting area.

Figures

Hypothesized Research Mode

Figure 1.

Hypothesized Research Mode

Conceptual Model of Moderated Mediation

Figure 2.

Conceptual Model of Moderated Mediation

Plots Explaining Moderation Effects of Risk

Figure 3.

Plots Explaining Moderation Effects of Risk

Definitions of constructs and sources of its measures

Sl. no Construct Definitions Sources of measures
1 Website attributes The perceived feeling about the ability of various attributes of an e-commerce website to evoke value feel to customers Aladwani and Palvia (2002), Ahn et al. (2007), Ganesh et al. (2010), Chiu et al. (2014), Wells et al. (2011)
2 Product The perceived feeling about the ability of various product attributes in an e-commerce marketplace to evoke value feel to customers Chiu et al. (2014), Pavlou (2003), Wu and Li, 2018
3 Promotion The perceived feeling about the ability of various promotional measures in an e-commerce marketplace to evoke value feel to customers Chiu et al. (2014), Wu and Li, 2018, Young Kim and Kim (2004), Park and Lennon (2009)
4 Decision easiness The perceived feeling about the ability of various attributes of an e-commerce website to evoke value feel to customers by making the purchase decision easy and confident Young Kim and Kim (2004), Park and Kim (2003), Srinivasan et al. (2002)
5 Hedonic value The value perceived by a customer from the fun and playfulness experience in online shopping Wolfinbarger and Gilly, 2001, Ganesh et al. (2010), Chiu et al. (2014)
6 Utilitarian value The value perceived by a customer from the task-related or rational shopping experience in online Wolfinbarger and Gilly, 2001, Ganesh et al. (2010), Ahn et al. (2007), Chiu et al. (2014)
7 Impulsiveness The urge developed in the mind of a customer to engage in a purchase behavior after visiting the e-commerce marketplace Rook and Fisher (1995), Parboteeah et al. (2009), Wells et al. (2011)
8 Risk The degree to which an e-commerce customer perceives uncertainties related to online purchases Miyazaki and Fernandez (2001), Pavlou (2003), Chiu et al. (2014)
9 Purchase completion intentions The intent developed in the minds of a customer to complete purchases online without abandoning the cart Chiu et al. (2014), Hong et al., 2011

Results of discriminant analysis

Constructs hedonic impulsi promo decieas risk prd compint utility webattr
hedonic 0.841
impulsi 0.683 0.741
promo 0.719 0.698 0.809
decieas 0.73 0.634 0.698 0.753
risk 0.065 0.089 0.028 0.043 0.622
prd 0.718 0.65 0.687 0.535 0.035 0.737
compint 0.709 0.549 0.689 0.646 0.069 0.637 0.799
utility 0.752 0.664 0.71 0.689 −0.031 0.691 0.733 0.782
webattr 0.74 0.735 0.696 0.549 −0.067 0.565 0.658 0.589 0.769
Note:

Square roots of average variances extracted (AVEs) shown on diagonal

Model estimation results

Criteria Value limit VIF Mean
Average path coefficient (APC) 0.188 <0.001
Average R-squared (ARS) 0.686 <0.001
Average adjusted R-squared (AARS) 0.673 <0.001
Average block VIF (AVIF) acceptable if <= 5, ideally <= 3.3 2.798 acceptable if <= 5, ideally <= 3.3
Average full collinearity VIF (AFVIF) acceptable if <= 5, ideally <= 3.3 3.276 acceptable if <= 5, ideally <= 3.3
Tenenhaus GoF (GoF) small >= 0.1, medium >= 0.25, large >= 0.36 0.589 small >= 0.1, medium >= 0.25, large >= 0.36
Indicator weights
Constructs Weights SE P-value VIF Mean SD
Completion intentions (AVE = 0.638; FVIF = 3.323)
Acting upon urges gives happiness 0.287 0.014 <0.001 1.477 3.442 0.997
Look for rationality in urges 0.328 0.017 <0.001 1.901 3.37 1.066
If benefits sure will purchase 0.331 0.019 <0.001 1.996 3.208 1.033
If process is simple will complete 0.304 0.014 <0.001 1.639 3.305 0.986
Impulsiveness (AVE = 0.547; FVIF = 3.355)
Displays 0.209 0.015 <0.001 1.489 3.422 0.982
Benefit perceptions 0.219 0.02 <0.001 1.646 3.416 0.975
Fear of losing offers 0.221 0.015 <0.001 1.6 3.429 1.059
Recommendations 0.214 0.012 <0.001 1.601 3.578 1.021
Sudden need arousal 0.238 0.018 <0.001 1.868 3.403 1.02
Achievement feel 0.246 0.02 <0.001 2.157 3.39 0.986
Risk (AVE = 0.517; FVIF = 1.896)
Logistic risk 0.331 0.042 <0.001 1.182 3.818 0.896
Financial loss 0.321 0.039 <0.001 1.285 3.409 0.988
Product mismatch 0.332 0.051 <0.001 1.278 3.838 0.987
Privacy loss 0.339 0.074 <0.001 1.253 4.175 0.801
Anxiety 0.282 0.045 <0.001 1.239 4.045 0.819
Utility (AVE = 0.612; FVIF = 2.996)
Convenience 0.303 0.017 <0.001 1.465 3.253 1.106
Easiness 0.334 0.018 <0.001 1.723 3.468 1.086
Privacy in shopping 0.324 0.016 <0.001 1.64 3.487 0.998
Easiness to quit 0.317 0.016 <0.001 1.555 3.448 1.067
Hedonic (AVE = 0.707; FVIF = 3.396)
Fun 0.372 0.014 <0.001 1.468 3.273 1.08
Enjoyment 0.414 0.019 <0.001 1.985 3.565 0.97
Relaxation 0.402 0.016 <0.001 1.836 3.487 1.011
Web attributes (AVE = 0.591; FVIF = 3.12)
Navigational easiness 0.278 0.019 <0.001 1.96 3.468 1.004
Usability 0.272 0.016 <0.001 2.076 3.63 0.936
Layout 0.268 0.016 <0.001 1.888 3.403 1.051
Information quality 0.233 0.013 <0.001 1.528 3.435 1.047
Technical excellence 0.247 0.017 <0.001 1.615 3.468 1.011
Promotional aspects (AVE = 0.655; FVIF = 2.884)
Offers 0.279 0.014 <0.001 1.463 3.305 1.062
Free shipping 0.312 0.014 <0.001 1.842 3.662 1.024
Loyalty points 0.325 0.017 <0.001 2.011 3.526 1.011
Easy payment options 0.318 0.017 <0.001 1.88 3.448 0.997
Decision easiness (AVE = 0.567; FVIF = 4.274)
Comparison 0.309 0.025 <0.001 1.303 3.266 1.079
Reviews 0.362 0.026 <0.001 1.674 3.63 0.983
Suggestions 0.336 0.023 <0.001 1.474 3.552 1.036
Categorization 0.318 0.023 <0.001 1.368 3.617 0.978
Products (AVE = 0.543; FVIF = 2.396)
Variety 0.285 0.021 <0.001 1.609 3.435 1.047
Descriptions 0.283 0.023 <0.001 1.686 3.539 0.957
Presentation style 0.285 0.02 <0.001 1.707 3.506 1.005
Availability 0.227 0.025 <0.001 1.273 3.597 1.032
Brand 0.273 0.018 <0.001 1.513 3.526 0.978

Results of hypotheses about direct effects

Hypothesis β value St. error p-value Supported
H1a: Web attributes to utility 0.301 0.112 <0.05 Yes
H1b: Promotion to utility 0.245 0.105 <0.05 Yes
H1c: Products to utility 0.161 0.076 <0.10 Only at 0.1 level
H1d: Decision easiness to utility 0.242 0.082 <0.05 Yes
H2a: Web attributes to hedonic 0.277 0.087 <0.05 Yes
H2b: Promotion to hedonic 0.281 0.093 <0.05 Yes
H2c: Products to hedonic 0.126 0.046 <0.05 Yes
H2d: Decision easiness to hedonic 0.207 0.078 <0.05 Yes
H3a: Utility to impulsiveness 0.116 0.046 <0.05 Yes
H3b: Hedonic to impulsiveness 0.143 0.034 <0.05 Yes
H3c: Utility to completion intentions 0.412 0.042 <0.05 Yes
H3d: Hedonic to completion intentions 0.118 0.083 <0.05 Yes
H4: Impulsiveness to completion intentions 0.392 0.076 <0.05 Yes

Effects of mediation from exogenous variables to impulsiveness

Path Indirect effect LLCI ULCI SE Total effects p SE Direct effect p SE Direct effect is significant in all cases
webattri-utility-impuls 0.175 0.033 0.351 0.081 0.738 <0.05 0.055 0.564 <0.05 0.087
promo-utility-impuls 0.242 0.129 0.378 0.063 0.699 <0.05 0.0.058 0.457 <0.05 0.077
prd-utility-impuls 0.30 0.156 0.441 0.073 0.660 <0.05 0.061 0.359 <0.05 0.098
decieasiness-utility-impuls 0.334 0.192 0.474 0.072 0.641 <0.05 0.062 0.307 <0.05 0.096
webattri-hedonic-impuls 0.1269# −0.192 0.286 0.078 0.738 <0.05 0.0547 0.6116 <0.05 0.077 direct effect is significant in all cases
promo-hedonic-impuls 0.1797 0.053 0.293 0.039 0.699 <0.05 0.058 0.519 <0.05 0.074
prd-hedonic-impuls 0.209 0.099 0.324 0.058 0.660 <0.05 0.061 0.451 <0.05 0.079
decieasiness-hedonic-impuls 0.256 0.118 0.341 0.056 0.6414 <0.05 0.062 0.416 <0.05 0.08
Note:

#insignificant direct effect

Effects of mediation from shopping motivations to impulsiveness

Path Indirect effect LLCI ULCI SE Total effects p SE Direct effect p SE Impulse to completion effect is significant
Utility-impulse-completions 0.310 0.210 0.410 0.051 0.735 <0.05 0.055 0.425 <0.05 0.063
Hedonic-impulse-completions 0.339 0.234 0.444 0.054 0.659 <0.05 0.061 0.321 <0.05 0.063

Effects of mediation from exogenous variables to purchase completion intentions

Path Indirect effect 1 LLCI ULCI Indirect effect 2 LLCI ULCI Indirect effect 3 LLCI ULCI
webattri-hedo-impulsive-comple 0.208 0.093 0.321 0.316 0.201 0.455 0.066# −0.012 0.153
promo- hedo-impulsive comple 0.155 0.064 0.239 0.229 0.135 0.334 0.079 0.036 0.136
prd- hedo-impulsive comple 0.18 0.808 0.286 0.226 0.131 0.331 0.105 0.045 0.177
decieasiness- hedo-impulsive comple 0.161 0.066 0.259 0.201 0.107 0.314 0.109 0.055 0.174
webattri-utilty-impulsive-comple 0.362 0.243 0.466 0.295 0.174 0.429 0.063 0.011 0.122
promo- utilty -impulsive comple 0.277 0.169 0.385 0.160 0.089 0.242 0.082 0.034 0.147
prd- utilty -impulsive comple 0.309 0.203 0.421 0.264 0.159 0.325 0.075 0.032 0.135
decieasiness- utilty -impulsive comple 0.291 0.186 0.406 0.239 0.126 0.354 0.065 0.027 0.122
Notes:

#Insignificant indirect effect.

Indirect effect1 = Exogenous variable * Hedonic/Utilitarian * Completion intentions.

Indirect effect 2 = Exogenous variable * Impulsiveness * Completion intentions.

Indirect effect 3 = Exogenous variable * Hedonic/Utilitarian * Impulsiveness * Completion intentions

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Further reading

Liao, C., To, P.L., Wong, Y.C., Palvia, P. and Kakhki, M.D. (2016), “The impact of presentation mode and product type on online impulse buying decisions”, Journal of Electronic Commerce Research, Vol. 17 No. 2, pp. 153-168.

Smith, A.D. and Rupp, W.T. (2003), “Strategic online customer decision making: leveraging the transformational power of the internet”, Online Information Review, Vol. 27 No. 6, pp. 418-432.

Corresponding author

Rejikumar G. can be contacted at: drrejikumarg@gmail.com

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