Examining role of perceived customer value in online shopping.

Tapar, Archit Vinod ; Dhaigude, Amol Subhash ; Tiwari, Santosh Kumar 等

Abstract

With the increasing penetration of Internet, online transaction is fast becoming an important mode of shopping, owing to factors like availability of wide varieties of products at cheaper rates and unparalleled convenience in today's fast-paced life. In an online setting customer are exposed to various kinds of risks, like performance, psychological, financial, social, online payment, and delivery risk. The merchant needs to overcome shoppers' perceived risk and increase their purchase intentions. Based on the extant literature, this paper provides an empirical evidence for the vital role the perceived customer value plays between risk and purchase intention, in online shopping.

Keywords: perceived risk, perceived customer value, purchase intention, online shopping.

JEL Classification: M39; O33; D81

INTRODUCTION

E-commerce has seen a tremendous growth in the recent years. Many factors have contributed to this unprecedented growth like -increased internet and smart phone penetration, time saving, availability of varied and cheaper products, convenience and ease of shopping, no pressure from the salesperson, etc. [McQuitty and Peterson, 2000; Szymanski and Hise, 2000]. As on December 2014, the valuation of Indian e-commerce market was INR. 8.15 billions [Internet and Mobile Association of India, IAMAI].

The increasing cross-border business prospects in the online market space is making the e-commerce more lucrative for the retailers [e.g. Donthu and Garcia, 1999; Lynch, Kent, and Srinivasan, 2001]. But this shift from offline to online shopping has posed uncertainty and issues to the consumers related to privacy, product-quality, delivery, etc. Such worries lead to the build-up of perceived risk in shoppers' buying decisions [Cases, 2002].

There are many studies that have discussed the role of website as a risk reduction function in an online shopping [e.g., Jiuan 1999, Cases 2002, Park and Kim, 2003]. Most studies have also dealt with identifying the association between consumers' perceived value and purchase intention in online context [e.g. Yang and Peterson 2004, Hsinand Wang, 2011], Sweeney, Soutar, and Johnson [1999] examined the role of consumer's perceived risk within quality-value relationship in retailing context and identified the need for examining the association in different shopping methods, one of them being the online retailing. Chen and Dubinsky [2003] also stated that buying online may not only lead to changes in the perceived customer value but also factors influencing it. Thus, our focus of study would be to address the existing gap in the literature and add clarity to the risk perception and purchase intention relationship.

OBJECTIVE OF STUDY

The present study focuses on customers' perceived value of website as a mediator through which perceived risk will affect the purchase intention of consumers while shopping online.

The study addresses the following research questions:

* Does consumers' perceived value of websites have any impact on association of purchase intention and consumer perceived risk in online setting?

* Does consumers' perceived value of websites have any impact on association of purchase intention and different types of perceived risk?

The following parts of the paper consist of theoretical understanding of concepts, the propositions derived based on the above objectives of the study, research methodology to be adopted for the study, and finally the implication, limitation, and future research directions.

THEORETICAL BACKGROUND

Perceived Risk

Robert Bauer was the first to introduce the concept of 'perceived risk' in the area of consumer behavior research. In his opinion "any action of the consumer will produce consequences which he / she cannot anticipate with anything approximating certainty, and some of which are likely to be unpleasant" [Bauer 1960, p. 24]. This uncertainty leads to risk perception amongst the consumer during purchase. Bauer [1960] emphasized that consumer behavior is influenced by "perceived risk" (or subjective) and not by a "real world risk" (or objective). "Perceived risk refer to the nature and amount of risk professed by a consumer in contemplating a particular purchase decision" [Cox and Rich, 1964, p. 33]. While purchasing goods or services in ecommerce setting, consumers are exposed to additional risk over the conventional [brick-and-mortar] risk owing to lack of personal contact, intangible and remote nature of transactions [Cases, 2002].

The extant literature on risk [e.g., Jacoby and Kaplan, 1972; Schiffman and Kanuk, 1994; Kurtz and Clow, 1997] has essentially discussed four dimensions of risk--(i) performance (ii) financial (iii) psychological, and (iv) social risk. In online shopping, delivery risk is an additional risk that we would consider for the present study. Delivery risks refer to risk arising out of inconsistency between the product that is ordered and the product being delivered [Ward and Lee, 2000]. Furthermore, consumers may perceive risk while paying online through debit, credit, or online banking, as they are required to share personal information while executing the payment.

Perceived Customer Value

"Perceived value is the consumer's overall assessment of the utility of a product based on perceptions of what is received and what is given" [Zeithaml 1988, p. 14]. The extant literature views perceived customer value as a trade-off between relative price vis-a-vis relative quality [e.g. Monroe, 1990; Gale, 1994], Sinha and DeSarbo [1998] criticized this simplification which ignores few key constructs that includes risk, shopping experience, and so on. Chen and Dubinsky [2003] defined perceived customer value "as a consumer's perception of the net benefits gained in exchange for the costs incurred in obtaining the desired benefits" (p. 326). Chen and Dubinsky [2003] also stated two important reasons to examine pre-purchase perceptions of consumer value in online shopping. First, customers spend considerable effort in evaluating options while making a purchase decision. Second, the perceived customer value has considerable impact on intention to purchase.

HYPOTHESIS DEVELOPMENT

Perceived Risk and Perceived Value

As discussed earlier, there are six different dimensions of perceived risk. These dimensions are defined in Table-1. Many researchers [e.g., Shimp and Bearden, 1982; Sweeney, Soutar, and Johnson, 1999; Teas and Agarwal, 2000] have suggested that consumers' perceived risk is a crucial variable and necessitate examination with regard to perceived customer value. Broydrick [1998] stated that to enhance perceived customer value, one of the important ways is to remove risk. Some empirical findings [e.g. Sweeney et al., 1999; Chen and Dubinsky, 2003] provide support to the role of perceived risk in value perceptions. Sweeney et al. [1999] argued that in the retail shopping setting, perceived risk has direct negative relationship with perceived value.

Perceived Value and Purchase Intention

The above definition of perceived customer value provided by [Zeithaml 1988] indicates consumers' overall gain received from their consumption pattern. Therefore, it is possible to use perceived customer value as an antecedent to purchase intention. Extant literature supports positive relationship between perceived value and consumers' purchase intention [e.g. Zeithaml, 1988; Monroe, 1990; Chen and Dubinsky, 2003]. A meta-analysis by Rao and Monroe [1989] concluding that the positive relation between perceived value and purchase intention holds for often purchased moderately priced goods.

Perceived Risk and Purchase Intention

Extant literature supports negative relationship between the overall perceived risk and purchase intention [e.g. Jiuan, 1999; Cases, 2002; Hong and Cha, 2013]. In the context of Internet shopping, the theory of planned behavior predicts that though a consumer's attitudes with respect to the online store are not positive, he/she is prone to make a purchase from an online shop which he/she perceives as low on risk [Jarvenpaa, Tractinsky, and Vitale, 2000]. Pavlou [2003] argued that perceived risk has negative relationship with purchase intention.

He further says that purchase intentions are prejudiced by beliefs about e-tailors that may direct to risk perceptions. Hong and Cha [2013] argued that negative relationship existing between perceived risk and purchase intention is expected to remain same for the individual dimensions (psychological, performance, financial, psychological, social, delivery, payment) of perceived risk. However, the degree of impact may differ from product categories to consumer segments [Hong and Cha, 2013].

Extending the same line of arguments, we posit following hypotheses:

H1: Perceived customer value of the website will mediate the relationship between performance risk and purchase intention

H2: Perceived customer value of the website will mediate the relationship between psychological risk and purchase intention

H3: Perceived customer value of the website will mediate the relationship between social risk and purchase intention

H4: Perceived customer value of the website will mediate the relationship between financial risk and purchase intention

H5: Perceived customer value of the website will mediate the relationship between online payment risk and purchase intention

H6: Perceived customer value of the website will mediate the relationship between delivery risk and purchase intention

[FIGURE 1 OMITTED]

RESEARCH METHODOLOGY

As the objective is to establish relation between the variables, the survey design is an appropriate method to collect the data points. Following section covers more detailed review of the methodology.

Measures

To measure different type of risks established scales, used by Featherman and Pavlou [2003] and Jarvenpaa and Todd [1997], were taken in our study. Each of these scales has three items. For the purchase intention all the three items used were drawn from Jarvenpaa et al. [2000], and Pavlou [2003]. A three-item scale for perceived customer value was taken from study done by Dodds, Monroe, and Grewal [1991]. Similar scale of perceived customer value was used in the study conducted by Sweeney et al. [1999] in retailing context. Chang, Wang, and Yang [2009] also used the same scale in online retailing context.

All the scales are found to have desired psychometric properties as per the respective authors. All the above items were measured on a seven-point Likert scales ranged from 1 (strongly disagree) to 7 (strongly agree).

Sampling and Procedure

Sample size: The recommended item-to-response ratio has to be at least 1:10 [Citing Schwab 1980, Hinkin 1995, p. 973], However, to check medium effect of mediation 100-sample size is sufficient [MacKinnon et al. 2002], In our study, we had 126 samples collected from a premiere management institute from central India.

Sampling technique: The present study follows purposive sampling technique.

Data collection: The data would be collected from the students of premiere institution in India through a self-administered 24-item questionnaire. The usage of student sample for such studies has been used in many previous empirical works, which have suggested that students form a good alternative for online consumers [e.g. Bhatnagar, Misra, and Rao, 2000; Jarvenpaa et al., 2000; Pavlou, 2003],

ANALYSIS OF RESULTS

We use regression model to test the hypothesis [Preacher and Hayes, 2004]. As we are also trying to see the mediation effect of customers' perceived value, we would need to use regression to identify the effect [Baron and Kenny, 1986; Hayes, 2009], Though the causal steps approach [Baron and Kenny, 1986] is popular for testing mediation effect, but recently it has attracted criticism from many scholars [Zhao, Lynch, and Chen, 2010], Given the criticism, to test the mediation effect, we use process tool for SPSS 18 as prescribed by Hayes, Preacher, and Myers [2011], PROCESS is a flexible program developed by Hayes [2012], which is now widely being used for testing indirect or the mediation effects [Hayes, 2009; MacKinnon, Fairchild, and Fritz, 2007], The advantage with using PROCESS lies in the fact that it facilitates quantification of mediation effect by the help of bootstrapping.

To test indirect effects, bootstrapping procedure (with n= 5000 resample) and 95% CI were employed. Mediation effect has been estimated by running process for each of the independent variable. But as noted by Hayes, "mathematically, all resulting paths, direct, and indirect effects will be the same as if they had all been estimated simultaneously [as in a structural equation modeling program]" [Hayes, 2013, p. 196], Hayes [2009] suggests that mediation effect should be investigated through indirect effects. Indirect effects are statistically significant when zero doesn't lie between their confidence intervals range. From Table-2 it is evident that perceived customer value is mediating for delivery risk and social risk with purchase intentions. Hence, the data support the hypotheses three and six.

IMPLICATIONS

This paper attempts to address the need stated by Chen and Dubinsky [2003] to examine the impact of pre-purchase consumer value perceptions in e-commerce with regard to purchase intention of the online shoppers. The study empirically strengthens the relationship between risk and purchase intention with the perceived customer value. The study also enhances the understanding of different aspects of risk in an online context. The result provides indicative areas such as delivery and social risk for online retailers to enhance their service offerings.

From a managerial perspective, it may be difficult to reduce the perceived social risk in short span of time, but the adverse effect of social risk can be mitigated by enhancing the perceived customer value. In the similar vein, the perceived customer value would also dampen the negative impact of delivery risk. To enhance the perceived customer value, managers can work on ways to improve perceived service and product quality along with better relative pricing.

LIMITATIONS

Though necessary precautions were taken, however there are few limitations. The present study uses student's sample as the respondent set for the study. Eliminating the use of student sample and using non-student sample may add value to the existing study. Effects of repurchase have not been considered in the present study. Adopting different category of e-tailors and studying their effect of risk perception can be carried out.

FUTURE RESEARCH

Apart from risk, the associated stigma with regard to online shopping as an important cue for risk and purchase dimension could be studied. Consumer knowledge of technology and factors relating to technology has been found to influence consumer behavior [Bahl, Black and Murphy, 2014], These technological factors may be considered in the online shopping context along with risk and purchase intention relationship. Different antecedents of customer perceived value could be studied in the further researches. The effects of global service providers on consumers' purchase intention could be analyzed through the same framework considering more complex scenario.

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TAPAR, ARCHIT VINOD, Research Scholar (Marketing Management), Indian Institute of Management Indore, Rau-Pithampur Road, Indore, Madhya Pradesh, India, E-mail: [email protected]

DHAIGUDE, AMOL SUBHASH, Research Scholar (OM&QT), Indian Institute of Management Indore, E-mail: [email protected]

TIWARI, SANTOSH KUMAR, Research Scholar (Strategic Management), Indian Institute of Management Indore, E-mail: [email protected]

JAWED, MOHAMMAD SHAMEEM, Research Scholar (Finance & Accounting), Indian Institute of Management Indore, E-mail: [email protected]

Table 1
Definition of different dimensions of perceived risk

Dimension              Definition

Performance risk       "Performance risk was defined as the likelihood
                       of problems associated with purchasing
                       unfamiliar brands or defective products."

Psychological risk     "Psychological risk was defined as the
                       likelihood of an insufficient fit between the
                       purchased product and the consumer's self-
                       image or self-concept."

Social risk            "Social risk was defined as the likelihood of
                       the purchased product influencing others' view
                       of the consumer."

Financial risk         "Financial risk was defined as the likelihood
                       of some financial loss resulting from
                       overpriced products, online fraud, or from
                       unexpected expenses (e.g., a 15% restocking
                       fee)."

Online payment risk    "Online payment risk refers to the likelihood
                       that a consumer's private information,
                       including personal and credit card information,
                       may be exposed to potential threats, and that
                       such private information may be misused."

Delivery risk          "Delivery risk was defined as the likelihood of
                       a delivery problem (e.g., late delivery of
                       products, delivery to a wrong address, and
                       delivery of a wrong product)."

Source: Hong and Cha [2013]

Table 2
Bootstrap coefficient, standard errors and u lower and upper intervals
for mediation

Variable                 Effect      SE      BootLLCI    BootULCI

Online payment risk      -0.098     0.07      -0.239       0.035
Delivery risk            -0.195    0.059      -0.317      -0.081
Performance risk         0.038     0.078      -0.097       0.213
Social risk              -0.219    0.064      -0.351      -0.104
Psychological risk       -0.229    0.055      -0.344      -0.129
Financial risk           -0.116    0.055      -0.218       0.002

Effect: Bootstrap coefficient; SE: Standard Error; BootLLCI:_Bootstrap
Lower Limit Confidence Interval; BootULCI: Bootstrap Upper Limit
Confidence Interval

Source: Compiled by authors