Mitigating Risk in E-Shopping: a Structural Modeling Approach

INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9, ISSUE 02, FEBRUARY 2020 ISSN 2277-8616

Mitigating Risk In E-Shopping: A Structural Modeling Approach

Sanjeev K. Sharma, Pooja Chopra

Abstract : The aim of this study is to formulate and test the empirical model to discover the association between various constructs of perceived risk and e-shopping intentions. A structured questionnaire was framed to collect data from four major metropolitan cities of India, namely Delhi, Kolkata, Bengaluru and Mumbai. Data was analyzed using descriptive statistics and structural equation modeling approach. The results revealed that perceived risk had a significant impact on e-shopping intentions. Accordingly, e-tailers are suggested to consider customer’s risk while designing their business strategies. Risk mitigating strategies are recommended for gaining consumers’ confidence and increasing e-shopping intentions to enhance their success in e-tailing business as reluctance to do so might lead to loss in business.

Key Words : Association, Consumers’ Confidence, e-shopping Intentions, e-tailing, Perceived Risk, Risk Mitigating Strategies, Structural Equation Modeling. ——————————  ——————————

1 INTRODUCTION Indian e-commerce trade has shown growth trajectory and is Technology based innovations have opened new vistas as a forecasted to surpass US to become the second largest e- paradigm shift is taking place in the retail industry landscape. commerce market across the globe by 2034 [7]. It is projected Retailers are embracing the digital technologies with open that by 2026, the basic norm followed by millennials to surf arms. Digitally influenced shopping has revamped the way online shopping site would be replaced by round-the-clock business is done and is fast gearing up to spur the demand for accessibility and quick turnaround time [8]. Millenniums, goods sold online. Conventional brick and mortar outlets are irrespective of their demographics, are adopting internet to orchestrating to become brick and click stores to offer keep pace with latest trends and fads through e-shopping [9]. merchandise that cater to ever increasing demands of Internet has dramatically revolutionized the horizons of both consumers [1]. Through omnichannel presence, retailers try to consumers and marketers in terms of acquiring, processing harmonize the touch points propelling a seamless buying and disseminating the information [10]. Online sites are no experience [2]. e-shopping is one of the power house driven longer solely confined to retail giants like Shoppers Stop, by modern technology to heighten the consumer’s interest and Lifestyle, Landmark, etc. but eventually small retailers too are participation. It has revolutionized the way shopping is done inching forward to explore the vast reservoir through online due to cheaper data rates, increased mobile phone medium for increasing their visibility and reach among masses penetration, improved logistics, comfortable payment [11]. e-Shopping is a big respite to shopper who don’t like to mechanism and is destined to increase manifold. A platform queue up in long checkout lines, roam around in crowed has been developed for mammoth US $1.9 trillion e-shopping shopping malls or get struck up in parking lots [12].It is market to blossom in leaps and bounds across the globe [3]. estimated that mighty leap would be possible owing to huge Technology based inventions like digital advertisements, real- potential for growth in tier-2 and tier-3 cities wherein brick and time logistic statistics, data driven marketing, integrating info mortar model in organized retail is still struggling hard to carve graphics for enriching customer engagement and e-payments their presence. E-marketers are constantly scouting for would ensure exponential growth in this sector [4]. Internet opportunities and harnessing the digital platform to get wave has played a pivotal role in catapulting India’s digital connected and increase their visibility amongst millions of economy to flourish in both length and breadth. The fast- people just at the click of button [13]. However, extant growing e-commerce market is projected to surpass US$24 literature strongly validates the innumerable benefits of billion in 2017 to US $84 by 2021 [5]. However, due to strong internet usage to explore vital information about the product. growth prospects and widespread mushrooming of organized However, contrary to it, only handful of browsers actually retail in India, it is projected to be the third largest consumer makes final purchase as they link uncertainty with products market in the world and touch US$ 1.2 trillion by 2021 [6]. e- bought online [14], due to risk involved in buying online, lack of tailers are purposely targeting avid smart phone users to truth in service provider, stringent return policy. The main aim broaden their reach effectively by pouring huge investments to of this study is to enumerate varied components of risk that boost e-tailing. function as constraints in embracing e-shopping wholeheartedly by the consumers. This research tries to integrate varied aspects of perceived risk, especially in e- ———————————————— tailing along with discussing the implications of perceived risk on intention of consumers to go for e-shopping. • Sanjeev K. Sharma is Professor at University Institute of Applied Management Sciences, Panjab University. Chandigarh. India. E- mail: [email protected] 2. OBJECTIVES OF THE STUDY • Pooja Chopra is ICSSR Post-Doctoral Fellow at University Institute The prime motive of conducting this exploratory research was of Applied Management Sciences, Panjab University, Chandigarh, to gain insights about the nature of consumer’s risk perception India. E-mail: [email protected] linked with e-shopping and the understanding the association ] between perceived risk and e-shopping intention. Effort has

been made to recommend strategies to address the consumers’ concerns for mitigating the various risks linked

with e-shopping. theoretical evidence, the following hypothesis is postulated: H1: Performance Risk has significant influence on Perceived 3. THEORETICAL FRAMEWORK Risk. The concept of risk perception and risk handling behavior was pioneered by [15] pioneered. He also theorized the imperative 4.2 Time Risk nature of this construct at every stage of the consumer choice According to [31], managing time risk is one of the crucial making behavior. Risk is primarily perceived as the factor which consumers evaluate while doing shopping. Time amalgamation of uncertainty and the possibility of severe loss as a result of a wrong purchasing decision, time invested consequences in terms of losses if not evaluated diligently in searching, learning product usage or return mechanism [16]. [17] proposed that perceived risk is the function of [32]. Internet offers a plethora of facts and figures regarding subjective ambiguity of the outcomes and varies as per the varied products and services. No doubt that internet greatly circumstances that engulf a particular decision. [18] reduced the cost of acquisition of information but consumers highlighted that risk affects a consumer’s propensity to make a spend time on navigating the e-shopping websites, spend time buying decision. This theory has greatly helped in elucidating in learning the usage of e-retailing portals, wait for the the information-searching behavior of the consumers [19] anticipated response along with cognitive effort put forward in thereby helping in reducing the post purchase dissonance search process [33],[34]. Many a times, consumers spend through careful evaluation of risk associated with a particular time in learning how to make payments online or rectifying decision. This theory has aided marketers to step into erroneous transactions. Sometimes web site download speed customer’s shoes and view their apprehensions. However, in is too slow and checkouts take more than expected time, preview of consumer behavior, perceived risk is taken up as a influences online shopping adoption [35]. Despite the fact e- fine blend of uncertainty plus fear of losses associated while shopping is quicker than brick and mortar shopping, still contemplating a particular buying decision [11]. [20] pinpointed customer has to wait for the product to be delivered at home that perceived risk in not the sole explanatory factor to be [36],[37],[38] and sometimes face inconvenience when e- considered while making purchase decisions but at the same tailers do not honor the scheduled time frame. Based on these time its importance cannot be underestimated also. [21] assertions, the following hypothesis is put forward: highlighted that e-shopping is a technology based, non-store H2: Time Risk has significant influence on Perceived Risk. form of business to consumer marketing strategy. However, risk-aversive consumers are often less likely go for e-shopping 4.3 Privacy Risk [22]. Thus, this research paper is centered around the strong The privacy of personal and confidential information is theoretical base formed by theory of risk perception. Effort has fundamental essence in all business activities be it off line [39] been made to explore the risk factors that act as impediments or on-online context [40]. It has gauged significant importance for e-shopping. Strategic risk assessment helps in decision for consumers, businesses, and regulators [41] since its support mechanism for e-tailers, thereby facilitating focus on inception. Security breaches in form of unauthorized collection, influential parameters during the complex buying behavior usage of confidential and sensitive information of consumers’ exhibited by the consumers during purchase process. often lead to identity theft [42]. The toll of privacy loss is way beyond the benefits of e-shopping in terms of information fabrication resulting in offsetting ease, time, or monetary savings 4. REVIEW OF LITERATURE for the consumers [43]. Privacy invasions may pose serious The present study is aimed to decipher the key drivers of impediment for consumer to go for e-shopping [44]. Therefore, online shopping intention by building the foundation on theory the following hypothesis was asserted: of perceived risk. Various renowned researchers [14], [23] H3: Privacy Risk has significant influence on Perceived Risk. noted that various form of shopping risks act as deterrent in pursuing online transactions and thus impacting the selection 4.4 Financial Risk of shopping channels by the customers. Financial risk is also termed an economic risk [45]. It is linked to the deterioration in the monetary wellbeing of a customer 4.1 Performance Risk [46]. Money can be lost as the actual inspection of goods Performance risk is associated with buyer’s evaluative cannot be done; non receipt of ordered goods or inability to judgment regarding product’s non functionality, often due to return/replace the goods purchase through e-shopping [47]. the lack of assessing the product attributes prior to purchase Consumers are also apprehensive regarding paying online as [24],[21] especially in case of e-shopping. Even product hackers could trace their banking details and misuse them malfunctioning and non-performance also come under the [48]. Credit cards swindling, payment frauds and receiving ambit of performance risk [25]. As e-shopping is a form of non- counterfeit products are the most invoked concerns when store-based retailing, it becomes tedious for consumers to buying online. This deters browser to go for e-shopping and accurately evaluate the quality of physical goods [14], [26] as spending maximum amount from their wallet and be more they rely on limited information and visual cues [27] displayed particular regarding the websites from which they will shop on the website. Consumers encounter higher performance risk [49]. Apprehension regarding buying fake product in the name in case of e-shopping due to lack of sensory feelings like of genuine product force the consumer to think twice before touch, smell, feel or taste the product [11]. As customer cannot buying something valuable from internet. Thus, consumers’ try the product before purchasing [28] leaving the customer in sense of uncertainty regarding monitory loss stems chiefly dilemma concerning the functionality of the product. Moreover, from apprehension regarding financial risk [50]. Based upon e-shoppers often fear the hard time they might face while the above discussion, following hypothesis was proposed: returning or replacing the product bought online, if it does not H4: Financial Risk has significant influence on Perceived Risk. come up to their expectations [29] as intended or enjoy the benefits as promised by the e-tailer [30]. On the basis of above 3627 IJSTR©2020 www.ijstr.org INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9, ISSUE 02, FEBRUARY 2020 ISSN 2277-8616

online survey was to circumvent any unbiased data and target 4.5 Psychological Risk the right set of responses from internet users who had done e- Psychological risk inhibits the consumers to do e-shopping. shopping in the last one year. The data was collected through Psychological risk is linked to the user’s personality trait which non-random sampling from four major cities namely Delhi, is linked to his decision-making trait to either take or avoid Mumbai, Bengaluru and Kolkata. The survey questionnaire risks [51]. It is also referred to as inability to choose amongst was distributed through widely used social networking sites vast array of merchandise. Consumers often avoid online like Facebook and Snapchat ; Mobile messenger applications shops due to fear of losing self-image and self-concept such as WhatsApp and Viber. These survey mediums were [26],[30]. It is also related with customer’s perception carefully chosen as they have their widespread, acceptability regarding implication of making a wrong purchase that could and popularity in Indian sub-continent. The variables taken for leads to social stigma and fear of social disapproval as their this current study were measured using validated scales used referent groups would disown then for made a bad decision. in previous studies. Three items of time risk were adapted Findings of [52] also advocates that there is an inverse from [41]; six items of privacy risk were adapted from [59],[41]; association between psychological risk and consumers’ four items of financial risk were adapted from [60],[61]; four attitude towards for e-shopping. This led to the following items of financial risk were adopted from[62],[63]; three items hypothesis: of psychological risk was were developed from [64] and three H5: Psychological risk has significant influence on Perceived items of e-shopping intentions were adapted form [65] The Risk. detailed description of items for our current study and the supporting literature of each construct is illustrated in Table 2. 4.6 e-shopping Intentions The respondents were asked to evaluate the items of each [53] describes e-shopping intentions as a complex decision- construct on the basis of their past e-shopping experience by making process where consumer’s readiness is clubbed with selecting the rating on Likert scale, ranging from ―1‖ being willingness to complete the final transaction after requisite ―strongly disagree‖ to ―5‖ being ―strongly agree‖. However, for browsing and comparing through various online and offline the ease of collecting quality data, the questionnaire was portals [54]. In-depth understanding of consumer’s choice for drafted using english language which is the lingual franker shopping online can be gauged by behavioral, cognitive, [66]. The initial version of the questionnaire was refined after attitudinal and psychological pattern [55], [56]. However, e- pilot testing on 97 respondents. This refinement facilitated in shopping sites offer a wide array of services to consumers that polishing and refinement of our research instrument as well as facilitates in taking prudent decisions resulting in intention to in establishment of content, face and internal validity [67] use e-shopping as a medium for purchase [14]. Previous research studies highlight that consumers’ risk perception had 6 EMPIRICAL ANALYSIS an adverse impact on consumer’ se-shopping intentions [57], To solicit the demographic characteristics of the sample [58]. Higher the magnitude of risk, higher the discouragement structure, descriptive statistics were used. The reliability and towards e-shopping intentions. Therefore, the following validity of the construct was assessed using Cronbach alpha, hypothesis was posit: composite reliability and convergent reliability. SEM was H6: Perceived Risk has significant influence on e-shopping applied using AMOS 5.0 software package to test the intentions. hypothesis and evaluate the causal association between Based on the review of relevant literature, a conceptual model variables as well as to accredit the structural research model. for this study is proposed 6.1 Demographic Analysis To minimize data errors along with avoiding unbiased response, [68] suggested that the requisite sample size between 300 to 500 is considered appropriate for research purposes. Thereby a total of 430 self-administered questionnaires were sent out to consumers through social networking sites. After editing all returned questionnaires, about 373 questionnaires were found utilizable for analysis, showing a yield rate of 74.6%. The descriptive statistics of the sample reflects more composition of females (54.6%) than males (45.4%). Majority of the respondents (76%) are in 20-35 years of age category. About (67%) of the sample earned were from monthly income of Rs.5,00,001 – Rs10,00,000 per annum. Almost all respondents have achieved at least university degree/Bachelor degree. About 54% of the respondents are salaried. On the whole, the sample is skewed towards the young educated females and economically sound strata of the population. The results are consistent with the e- FIGURE 1: CONCEPTUAL FRAMEWORK shopping consumer profile in India.

5 SAMPLE 6.2 Goodness of Measure In order to test the study model and attain the desired Two necessary approaches to evaluate the goodness of objective of the study, a quantitative study using an online measure are reliability analysis and factor analysis [69]. Before questionnaire in the Indian context was drafted. The aim of the going for data analysis, the measurement instrument was

assessed for reliability using Cronbach alpha. Cronbach alpha tension e.g. Concerns about errors in operations A breakdown in e-shopping systems could cause helps to determine the degree to which the observed variables PsyR3 .812 measure the true and error free results in a multi item scale. unwanted anxiety and confusion. e- shopping Purchase Intentions [67] in his research suggested that the value of each construct PI1 I intend to buy through internet. .926 if more than 0.6 is considered reliable. However apart from It is probable that I would buy through internet in PI2 .924 this, Table 4 shows the Cronbach alpha ranges from depicting the future. a good internal consistency thereby providing a strong base PI3 I would buy through internet in the future. .901 for further analysis. The next step is evaluating the goodness of measure through factor analysis using varimax rotation in Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .879 order to determine the structure between the set of observed Bartlett's Test of Sphericity Approx. Chi-Square 2432.037 variables [40]. Kaiser-Meyer-Olkin measure of sample df 253 Sig. .000 adequacy was found to be 0.879 Thus, the value suggests that it was deemed appropriate to apply factor analysis. [68], 6.3 Evaluation of the Research Model suggested the factor loading greater than 0.5 is considered The empirical data was analyzed using Amos 21.0.A standard significant for an item to be included in its respective construct. two step approach was followed. Firstly, confirmatory factor Total of five factors with Eigen values > 1 were retained. analysis (CFA) was run to assess the measurement model for However, these 5 factors resulted in 76.8% variance. all the latent constructs followed by Structural Equation Model Communalities ranged from 0.50 and 0.75. (SEM) to test the hypothesis. Initially CFA was performed on each construct to check for factor loadings < 0.7 [68]. The Table 1: Dimensions, its variables and Factor Loading Factor overall CFA analysis (Table 1) shows that all factor loadings > Dimension and Its Variables Loadings 0.5 (ranging from 0.707 to 0.926) signifying adequate Privacy Risk individual item reliability [70]. Moreover, they were statistically I am concerned that the e-shopping sites signiﬁcant (P < 0.05), providing strong grounds for convergent PrcvyR1 providers collect too much personal information .841 validity as well [71], [72]. However, the research instrument from me. also exhibits discriminant validity as (AVE/(Corr2) is greater I am concerned the e-shopping sites will share PrcvyR2 my personal information with other entities .839 than or equal to unity in correlations with other constructs [73]. without my authorization. The overall goodness fit for the research model using I am concerned that unauthorized persons (i.e. covariance structure analysis was shown in Table 2. PrvcyR3 hackers) have access to my personal information .835 during e-shopping. Table 2: Average Variance Explained, Average Variance Extracted, I am concerned about the privacy of my personal PrcvyR4 .832 Composite Reliability and Cronbach Alpha information during e-shopping transactions. Averag I am concerned that the e-shopping sites will sell e Average Converge PrcvyR5 my personal information to others without my .811 Varianc Composit Cronba Variance nt permission. Dimension e e ch Explaine Reliability I am concerned that the e-shopping sites collect Extract Reliability Alpha PrcvyR6 .804 d (%) (>.07) too much personal information from me. ed Performance Risk (>.05) e-shopping might not perform well and create Privacy Risk 42.189 0.684 0.827 0.929 0.936 PerfR1 .786 problems with my finances. Performance Risk 12.453 0.568 0.753 0.840 0.914 The security systems built in the e-shopping sites Financial Risk 8.259 0.525 0.724 0.816 0.894 PerfR2 are not strong enough to protect my financial .771 Time Risk 4.282 0.626 0.790 0.833 0.873 accounts. Psychological The probability that something may go wrong 3.983 0.708 0.841 0.879 0.869 PerfR3 .749 Risk with the performance of e-shopping is high. e-shopping 9.390 0.841 0.917 0.941 0.919 It would be risky for me to sign up and use e- Intention PerfR4 shopping services and experience expected level .707 of service performance. 6.4 Analysis of Structural Model Financial Risk The chances of losing money if I buy through e- After assessing the proposed research model with satisfactory FR1 .749 shopping are high. results from CFA with the help of correlation matrix, second Using e-shopping will subjects my account to step i.e. Structure Equation Model {SEM} was developed FR2 .744 potential fraud. (Figure2) to elucidate the hypothesis and statistically validate My signing up for and using e- e-shopping sites FR3 .726 the proposed structure. On the basis of fit indices of our may lead to a financial loss for me. hypothesized model (Table 3), a reasonable fit between the Using e-shopping may subject my account to FR4 .678 financial risk. structural model and the underlying observed variables has Time Risk been observed [74]. The results support H1, H2, H3, H4, H5 Using e-shopping will lead to inconvenience proving that perceived risk is explained by Time Risk (TR), TR1 because I will waste a lot of time fixing payment .833 Psychological Risk (PsyR); Privacy Risk (PrcvyR), Financial errors. Risk (FR) and Performance Risk (PerfR). The results of the Considering the investment of my time involved, TR2 .778 analysis portray that all the five estimates were significant. it would be risky for me to switch to e-shopping. Table4 illustrates the hypothesis results and the standardized The possible time loss from having to set up and TR3 .760 learn how to use e-shopping is high. beta coefficients. Psychological Risk PsyR1 e-shopping will not fit well with my self-image. .889 PsyR2 Doing e-shopping would cause unnecessary .821 Table 3: Model Fit Indices

Name of Indices Measurement Model Structural Model This research attempted to dive deeper into the construct and 310.563 / 160 434.464/ 224 Chi Square/Degree of Freedom provide insight into its facets. The evidence from prior studies = 1.941 = 1.940 validates that behavioral intention closely relates with actual GFI (Goodness of Fit) 0.81 0.78 behavior pattern [75],[76],[77]. Therefore, assessing intention for AGFI 0.75 0.72 NFI 0.86 0.83 e-shopping would provide acceptable cues for understanding RFI (Relative Fit Index) 0.83 0.81 complex consumer buying pattern. Our results allow e-tailers to IFI (Incremental Fit Index) 0.93 0.91 craft strategies to hedge the various risk dimensions cited in the TLI 0.91 0.90 study. The analytical results illustrate that financial risk weighs CFI (Comparative Fit Index) 0.92 0.91 heavily on perceived risk. It is validated by [49] that the probability RMSEA (Root Mean Square 0.08 0.79 of perception of financial risk decreases as the experience of Error Approximation) shopping increases. Retailers should link up with preferred *Chi square/degree of freedom lower than 3 indicate a good fit of model **RMR, GFI, AGFI, NFI, RFI, IFI, CFI, TLI closer to 1 indicate a good fit of payment options which are well encrypted and authenticated model. such as payment via credit card etc. They should also provide ***The lower the RMSEA values, the better the model is considered customer friendly easy return and exchange policy of merchandise to infuse more inclination of the browser to do e- shopping. Proper training and awareness among the users will greatly help in minimizing their vulnerability to fall prey to phishing Table 4: Hypotheses Results and The Standardized Beta Coefficients attack. Moreover, e-tailers should shape their strategies in order Bets Standard Critical P to provide assurance to indemnify consumers against any Path coefficients Estimate coefficients Error Ratio Value financial frauds. Our findings concur with [78] who found that e- H1: Time Risk tailers should highlight the benefits of products along with giving 0.75 1.025 .148 6.933 *** an option of try and buy. The results of our study also highlight Perceived Risk that performance risk is also a key inhibitor in e-shopping. So, to H2: Psycological Risk curtail this, e-tailers may need to continually upload creative and 0.55 .642 .127 5.052 *** Perceived Risk informative videos relating to virtual product experiences and H3:Privacy Risk providing warranty/ guarantee in case of faulty products could 0.64 .971 .161 6.026 *** greatly help in minimizing any apprehensions related to product Perceived Risk performance [79]. Proper packaging and handling during H4:Financial Risk transportation would also enhance the product performance 0.89 1.000 Reference point (Claudia, 2012). The results are in consonance with the previous Perceived Risk studies of [80] and Hussain, et al., (2017). E-wom (electronic- H5:PerformanceRisk word of mouth) also can work as a creative tool that could help in 0.90 1.333 .153 7.389 *** Perceived Risk better buying decision and reducing the purchase regret [81],[82]. H6: Perceived Risk Results also highlight that time risk too is a hurdle in e-shopping. 0.24 .335 .137 2,443 .01* It is often seen that browser often on account of slow response e - Shopping Purchase time in opening images or shopping carts check out causes intentions (*** is significant at .001 level immediate user fatigue on the net. e-tailers need to understand *.05 is significant at .05 level) and work out strategies for navigational ease, faster checkouts, timely delivery of consignments and quick redressal to any erroneous transactions. e-tailers can reduce product search time by facilitating the customers with product information, toning with their tastes and preferences after analyzing browsing history along with customer’s pre purchasing tendencies. Privacy risk stems from the breach of trust in terms of personal confidential information by the e-tailers. They should clearly upload all the legal and regulatory clauses on their websites. e-tailers should work on building corporate credibility which will not only enhances trust online and reliability concerns but also serve as safety net against frauds [83]. e-tailers ought to be proactive in framing and implementing privacy policy so as to signal that store is concerned about the interest and well-being of shoppers. e-tailers should take prior approval from consumers regarding potential usage or transfer of personal data transfers to as to hedge them for personal and financial data privacy. Our results are consistent with that of [84],[85]. Psychological risk too is a pressing concern

for consumer as they undergo immense psychological discomfort due to anticipated post behavioral anxiety such as regret from Figure:2 Structure Equation Model purchasing and thereafter using the product. Usage of specific products or buying from certain websites might cause potential loss of self-esteem as peer pressure or social influences. e-tailers 7. DISCUSSION should work on building strong store image, as this will work as This paper provides a number of theoretical and practical catalyst in gaining trust and reduce psychological risk [86]. E- implications along with useful insights about multifaceted tailers ought to increase their in-depth understanding of the dimension of perceived risk especially in context of e-shopping. 3630 IJSTR©2020 www.ijstr.org INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9, ISSUE 02, FEBRUARY 2020 ISSN 2277-8616

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