Paradigm Shift from Attracting Footfalls for Retail Store to Getting Hits for E-stores: An Evaluation of Decision-making Attributes in E-tailing

Abstract

With the advent of e-tailing, there is a paradigm shift in job of the retailers, where the main concentration is on getting more hits to their website (e-store) rather than attracting the footfalls of customers to their retail outlets. In this changing retail landscape, an attempt has been made to find out the new touch points that influence e-sales. The basic objective of the study is to identify and evaluate the attributes of decision-making of online customers. The attributes in pre-purchase stage, purchase process stage and post-purchase stage of online shopping are listed out separately from the previous studies.

Keywords

E-tailing purchase decision consumer decision-making process online shopping factor analysis

Introduction

E-tailing, online retailing, online shopping, e-commerce, e-marketing, etc., are the buzzwords all over the world, where customers do not require to walk-in into stores, which does not have the physical boundaries, where the customers’ needs are satisfied by making the products available at their door step and so many. With the advent of these words, the decision-making process of the consumers has been changed. The consumer decision-making process has activities and actions of consumers in three stages, namely, pre-purchase stage, purchase stage and post-purchase stage. At every stage, there are certain attributes that will have influence on the consumer decisions. The job of the retail marketers is to identify those attributes and see that their store and their merchandise should be the final choice for the consumers to shop. A good number of concepts, theories and evidence are available for the retail managers to attract footfalls to the retail stores, but as still e-tailing is at its growth stage, there is a need for development of new concepts, theories and evidence that will be helpful for the e-tail managers to get hits to their e-stores. In this situation of changing retail landscape, an attempt has been made to assist the e-tailers to make them know what are the touch points of e-tailing those attract online customers to e-store and the attributes of consumer decision-making process in online shopping to redesign and realign the e-tail strategies. This research is categorized into various sections, that is, review of literature, objectives of the study, methodology of the study, model specification, analysis of the study, model testing and discussion, conclusions, limitations and scope for further research. The profile of the study area is described in Appendix 1.

Review of Literature

Doolin et al. (2005) defined that e-tailing was the sale of products and services to individual customers. According to him, the definition of e-tailing encompasses the sales of products or services online. Dunne and Lusch (2005, pp. 10–11) opined that one of the most dramatic changes e-retailing is creating is a shift in power between retailers and consumers. The Internet shopping fulfils several consumer’s needs more effectively and efficiently than conventional shopping (Grewal, Iyer & Levy, 2002). E-commerce offers increased market activity for retailers in the form of growing market access, information and decreased operating, procurement costs. The consumers can gain better prices due to the competition and also can enrich their knowledge on goods and services (Rao, 1999). Eroglu, Machleit and Davis (2001) analyzed that the challenges of traditional retailing are physical store setting, the cost of real estate and the various physical objects required for creating different sounds, aromas, colours and lighting, whereas Cao and Zhao (2004) analyzed that the challenges of e-tailing are the response time of the web-server, moves to the amount of time the customer must wait until the order ships and also include the time the shipping process takes. Yet another comparison between online and physical retailing by Guttman et al. (1998) and described that several unique elements that make online shopping different from the traditional shopping, besides offering convenience and expanded product variety, the online model also makes it easy for consumers to access and compare data from multiple sources. Another distinguishing view point, computer users, particularly those who seek online substitutes to the physical shopping experience would value aesthetic designs just like consumers of other commodities (Tractinsky & Rao, 2001). Das, Guin and Datta (2013) and Zott & Amit (2001) studied value creation on e-business and proposed four main drivers of value creators as efficiency, complementarities, lock-in and novelty. Forsytbe and Sbi (2003) studied the risk factors of online shopping and proposed that there are four types of risk with online shopping they are financial, product performance, time/convenience and psychological (privacy) risk impact. The security and privacy provided by online channels have frequently been questioned by consumers (Ranganathan & Ganapathy, 2002).

Throwing light on attributes of online shopping, Zeithaml (2002) had identified that the success of e-tailing depends on the efficient website design, effective shopping and prompt delivery. The other services attributed were delivery on real time, return and replacement process, period of filling out online orders form, speed of response time to e-customers’ queries. The studies of Betts (2001) and Burke (2002) indicated that online customers desire to know the availability of inventory and want fast and convenient checkout, secure ordering and the ability to track purchases. Shopping experiences can directly enhance value perceptions and store purchasing intentions (Kerin, Jain & Howard, 1992). Provision of comparative shopping (Grunert & Ramus, 2005), speed of accessing e-store and screen complexity (Kim, Lee, Han & Lee, 2002), value-added services (Reichheld & Schefter, 2000) and prompt delivery (Zeithaml, 2002) are other attributes of successful e-tailing. Kim et al. (2002) identified that the attributes those attract the online shoppers are website design, design of product and service comparison and information, time to complete online order form, ease of searching a product and service, screen layout, screen complexity, page composition, information retrieval methods, information display, use of colour and background, assistance to the user and speed of accessing the e-store. Kerin et al. (1992) found that shopping experiences can directly enhance value perceptions and store purchasing intentions. Zhang and von Dran (2000) identified the attributes of e-store attraction are the colour and background images of web pages.

In order to have deeper picture of these attributes, now these attributes are classified according to the stages of consumer decision-making process, that is, pre-purchase stage, purchase stage and postpurchase stage. Kiff (2000), Burke (2002) and Chen and Dubinsky (2003) applied the model of pre-purchase, acquisition and post-purchase phases, to understand consumers’ buying considerations within the context of e-retailing. The classic purchase decision model identified stages through which consumers proceed in making buying decisions (Perreault & McCartby, 2002). Ratchford, Talukdar and Lee (2001) stated that consumers can gather information about merchandise and they can compare products across suppliers at a low cost. They can also effectively analyze the offerings and locate a lower price for a specified product. Misra (2011) stated that in the consumer decision-making process, information search begins when consumers recognize a purchase problem. Further proposed a framework for creating customer value in e-retailing during different stages of purchase decision-making, the stages are pre-purchase stage deals with search and evaluation, purchase stage deals with transaction and purchase and post-purchase stage deals with post-purchase formalities. As the study is only conceptual one and there is no empirical evidence backing this framework. Peterson, Balasubramanian and Bronnenberg (1997) opined that consumers have the choice of searching for information from two channels. They can search for information exclusively online or offline, or in combination. In the purchase stage, shoppers are ready to buy and expect to be able to complete their transactions with a limited amount of effort (Ellis & Marino, 1992). The studies of Betts (2001) and Burke (2002) identified that online customers desire to know the availability of inventory, and want fast and convenient checkout, secure ordering and the ability to track. The consumers were getting smarter in using e-tailers (and online search engines and agents) for convenience and comparison shopping (Myerson, 1998). Zeithaml (1988) applied the cost of customer concept to web retailing stating that the time and effort invested are mediated by the perceptions of product value for money. Apart from time and effort, consumers can bear psychological or emotional costs in order to receive their products during purchase stage. At post-purchase stage, consumers compare their actual experiences with expectations and promises provided by the retailer, including the timeliness and reliability of product delivery (Kolesar & Galhraith, 2000). The customers were more likely to continue shopping online when they had a greater experience of online shopping (Delone & McLean, 2004). More attributes are shown in Table 1 according to their importance in online shopping decision-making process.

Table 1.

Identified Attributes of Online Shopping

Attributes	Author(s)
Pre-purchase Stage
Access to information	Peterson et al. (1997)
Availability of product information	Chen and Dubinsky (2003)
Availability of pricing information	Burke (2002)
Range of products	Zeithaml, Parasuraman and Malhorta (2000)
Minimum transaction cost	Liang and Huang (1998)
Array of product alternatives	Bakos (1991)
Information retrieval methods	Kim et al. (2002)
Interactive search process	Klein (1998)
Convenience	Misra (2011)
Ease of use	Misra (2011)
Purchase Process Stage
Limited amount of effort in online shopping	Ellis and Marino (1992)
Payment security	Gritzalis and Gritzalis (2001)
Availability of inventory	Betts (2001)
Fast checkout	Betts (2001)
Convenient checkout	Betts (2001)
Secure ordering	Burke (2002)
Online tracking of customer orders	Kolesar and Galbraitb (2000)
Speed of shipping	Peterson et al. (1997)
Costs of shipping	Peterson et al. (1997)
Period of filling out online orders form	Zeithaml (2002)
Screen layout	Kim et al. (2002)
Assistance to the user	Kim et al. (2002)
Efficient website design	Zeithaml (2002)
Speed of response time to e-customers queries	Zeithaml (2002)
Use of colour and background	Kim et al. (2002)
Page composition	Kim et al. (2002)
Shopping experience	Kerin et al. (1992)
Security and privacy	Cheung and Lee (2001)
Ease and convenience of shopping	Brown (1987)
Substitutability of personal examination	Cheung and Lee (2001)
Post-purchase Stage
Reliability of product delivery	Kolesar and Galhraith (2000)
Timeliness of product delivery	Burke (2002)
Channels to resolve customer service issues	Burke (2002)
Delivery of merchandise as ordered on time	Wang and Huarng (2002)
Channels to share customers’ experiences with others	Hagel and Armstrong (1997)
Return and replacement process	Zeithaml (2002)
Greater experience of online shopping	Delone and McLean (2004)
Post-purchase communication on order refund requests	Milliman and Decker (1990)
Complimentary benefits	Gale (1994)
Loyalty programmes	Misra (2011)
Product condition	Misra (2011)
After sales service	Zott and Amit (2000)
Customer support	Misra (2011)
Feedback	Misra (2011)
Post-purchase communication	Misra (2011)

Source: Compiled by the author (attributes identified from previous studies).

Objectives of the Study

The primary objective of the study is to identify the attributes of consumer decision-making process in e-tailing and then to determine the attributes that derive maximum satisfaction for online shoppers. The attributes of consumer decision-making process in e-tailing will be identified from previous studies of e-tailing and will be segregated into three categories, that is, pre-purchase stage, purchase stage and post-purchase stage.

Methodology of the Study

The study is mainly an empirical one and the variables used are both quantitative and qualitative in nature. The study is primarily based on primary data. The primary data are collected from 600 online customers after confirming that they performed online shopping for purchasing goods. The study aimed at getting the satisfaction levels of the online customers on identified attributes of online shopping in pre-purchase stage, during purchase stage and post-purchase stage. A structured questionnaire is designed in such a way that it captures the satisfaction levels of online shoppers at each stage.

Sampling technique: Judgement sampling

Sample size: 600

Study area: Visakhapatnam

Data collection instrument: A structured questionnaire

The Cronbach’s alpha value was calculated for the questionnaire administrated in order to determine the reliability of the data where the alpha value is greater than 0.70 is the recommended level (Bernardi, 1994). For this study, Cronbach’s alpha value is calculated as 0.793 for 600 sample which indicates that the data have relatively higher internal consistency (79.3 per cent).

The questionnaire was administrated in such a way that it carefully records the satisfaction levels. The customers were asked to provide their expectations on a five-point Likert scale (highly satisfied [5], satisfied [4], slightly satisfied [3], dissatisfied [2], highly dissatisfied [1]) regarding attributes of e-stores. Questions in the questionnaire were framed in such a manner that the respondent gives their opinion mostly for questions on a five-point Likert scale. The online customers approached for this study are presented in Table 2.

An analysis was made to identify which products and services online customers wish to purchase. The preferred products wish to purchase by the online customers are analyzed in this section. In the sample, 18.96 per cent of customers wish to purchase mobile phones and tablet PCs, 4.49 per cent wish to purchase laptops and desktops, 4.57 per cent cameras, 19.81 per cent footwear, 11.15 per cent apparels, 2.24 per cent jewellery and accessories, 30.11 per cent books and stationary and 8.67 per cent online shoppers wish to purchase personalized gifts through online shopping. Most of the customers wish to purchase books, footwear, mobile phones and tablets. The preferred services wish to purchase by the online customers are also analyzed. In the sample, 23.69 per cent of customers wish to purchase travel and tourism services, 20.51 per cent wish to purchase entertainment and cinema, 7.07 per cent salons and spa, 19.32 per cent hotels and hospitality, 8.9 per cent pubs and lounge, 20.51 per cent online shoppers wish to purchase banking and insurance services through online. Most of the customers wish to purchase travel and tourism services, banking and insurance, hotels and hospitality. The preferred e-store to shop by customers are analyzed, Flipkart for 17.45 per cent customers, Snapdeal 16.48 per cent, Mintmydeal 6.30 per cent, Rediff 4.36 per cent, Jabong 3.88 per cent, Naaptol 10.90 per cent, Myntra 18.5 per cent, Fashion and You 15.83 per cent, Amazon 3.965 per cent and eBay 2.34 per cent.

Table 2.

Preferences of Respondents

Variable	Categories of Variable	Frequency	%
Preferred Products wish to purchase online*	Mobile phones and tablet PCs	245	18.96
	Laptops and desktops	58	4.49
	Cameras	59	4.57
	Footwear	256	19.81
	Apparels	144	11.15
	Jewellery and accessories	29	2.24
	Books and stationary	389	30.11
	Personalized gifts	112	8.67
Preferred services wish to purchase online**	Travel and tourism	298	23.69
	Entertainment and cinema	258	20.51
	Salons and spa	89	7.07
	Hotels and hospitality	243	19.32
	Pubs and Lounge	112	8.90
	Banking and insurance	258	20.51
Preferred e-tailing Site***	Flipkart	216	17.45
	Snapdeal	204	16.48
	Mintmydeal	78	6.30
	Rediff	54	4.36
	Jabong	48	3.88
	Naaptol	135	10.90
	Myntra	229	18.50
	Fashion and You	196	15.83
	Amazon	49	3.96
	eBay	29	2.34

Source: Field data compiled by the author.

Note: Since the respondents gave their response for more than one option.

*Total = 1298; **Total = 1258; ***Total = 1238.

Empirical Model Specification

Factor analysis is used for the study to determine the attributes of online consumer behaviour that derive maximum customer satisfaction. It is a statistical technique used for determining the underlying factors or forces among a large number of interdependent variables or measures (Krishnaswami & Ranganatham, 2007). In social sciences and especially in behavioural studies, variables cannot be measured directly. Such variables are usually referred as ‘latent’ variables and can be measured by qualitative propositions to reflect the perceptions of the respondents. The factors generated are used to simplify the interpretation of the observed variables. Hair, Anderson, Tatham and Black (2006) well defined the meaning of factor loadings and scores in words. Factor loadings are the correlation of the original variables (e-tailing determinants) and indicate the degree of correspondence between the variable and the factor. Therefore, higher loadings make the variable representative of the factor, and loadings are the means of interpreting the role of each variable in defining each factor. In addition, squared factor loadings indicate what percentage of the variance in an original variable is explained by a factor. Factor score is a composite measure created for each observation (e-tailing attributes) on each factor. The factor score conceptually represents a few degrees of how much each observation is significantly related to a factor that consists of variables. Higher (lower) values on the variables with high loadings on a factor will result in a higher (lower) factor score. The association among factors and factor scores can be expressed as.

X_{i} = f_{i 1} S_{1} + f_{i 2} S_{2} + f_{i 3} S_{3} \dots \dots \dots \dots \dots \dots \dots f_{i n} S_{n} + U S_{i}^{u}

(1)

The common factor consists of f_i₁S₁ where f_i₁, f_i₂,…f_in are the factor loadings of factors (i = {1, 2, _•••, n}), respectively, and S₁, S₂……..S_n are the score of the corresponding factors for n. The specific factor is represented by $U S_{i}^{u}, a n d S_{i}^{u}$ is the score of the specific factor.

The objective of proposed model is to identify the attributes which have greater impact on overall customer satisfaction so that the e-tailers can concentrate on betterment of those attributes. For this study, the data is collected from online customers regarding their satisfaction levels towards attributes of online shopping. Theoretically, the model is specified as follows.

\begin{matrix} O v e r a l l C u s t o m e r S a t i s f a c t i o n (O C S) = f (\Pr e - p u r c h a s e S a t i s f a c t i o n (P R E), \\ P u r c h a s e S a t i s f a c t i o n (P U R), \\ P o s t - p u r c h a s e S a t i s f a c t i o n (P O S)) \end{matrix}

(2)

O C S_{i} = f_{i 1} P R E + f_{i 2} P U R + f_{i 3} P O S + U_{i}^{u}

(3)

With this model, it is attempted to find out that with which attributes of the online customers are satisfied. Factor analysis is performed on the data of attributes of customer satisfaction of pre-purchase stage (PRE), purchase process stage (PUR) and post-purchase stage (POS) separately. The attributes of all three stages will form the significant factors that influence the satisfaction in each stage. All these factors (PRE, PUR, POS) are considered as independent variables and the factor formed with overall satisfaction (OCS) is considered as dependent variable to test the model with multiple regression analysis. The overall satisfaction on attributes of each stage of the online purchase process is recorded separately and factor analysis was performed to form a factor of overall satisfaction that will be considered as dependent variable in the model.

Analysis of the Study

Levels of Customer Satisfaction towards Pre-purchase Attributes (PRE)

In this section an attempt has been made to analyze the levels of customer satisfaction towards the attributes of pre-purchase stage of online purchase process. Pre-purchase attributes influence consumer purchase decision-making process before start of the online purchase process. These attributes motivate the customers to shop in a particular e-store. The customers were asked to provide their satisfaction levels on a five-point Likert scale (highly satisfied [5], satisfied [4], slightly satisfied [3], dissatisfied [2], highly dissatisfied [1]) regarding 10 attributes which were derived from previous studies. To determine the data reliability, reliability test was performed on the data of customer satisfaction towards prepurchase attributes. The value of the Cronbach’s alpha is found to be 0.787, which shows the data of pre-purchase-attributes is 78.7 per cent reliable which ensures to proceed for further analysis.

Reliability of Data: Kaiser–Meyer–Olkin (KMO) and Bartlett’s Test for Pre-purchase Attributes

To know with which attributes the customers are satisfied, factor analysis was performed on the data of pre-purchase attributes. Since all these attributes are like dependent variables which influences the customer satisfaction, factor analysis seems to be appropriate tool. To determine the appropriateness of application of factor analysis on the identified attributes of pre-purchase stage, KMO and Bartlett’s test was performed as shown in Table 3. The KMO measure is observed to be 0.72 which is higher than the threshold value of 0.5 (Hair, Anderson, Tatham & Black, 1998). So, it can be interpreted that there is no error in 72 per cent of the sample and remaining 28 per cent, there may occur some sort of error. Bartlett’s test of sphericity ([γ² =1324.489] is found to be significant [p < 0.001, df = 45]) that measures the correlation of variables where the probability of less than 0.05 (p < 0.05) is acceptable (Anchaliya, Chitnis, Bapna & Shrivastava, 2012). Finally, it can be concluded that the data collected on pre-purchase attributes is appropriate for factor analysis.

Table 3.

KMO and Bartlett’s Test for Pre-purchase Attributes

Kaiser–Meyer–Olkin Measure of Sampling Adequacy		0.720
Bartlett’s test of sphericity	Approx. chi-square	1324.489
	df	45
	Sig.	0.000

Source: Factor analysis data reduction (SPSS 16.0).

Factors: Pre-purchase Attributes

Factor analysis was used to remove the redundant variables from the survey data and to reduce the number of variables into a definite number of dimensions. The application was done in SPSS. The factor analysis was performed using principal component extraction method with varimax rotation. After performing factor analysis, the 10 attributes were reduced to three factor dimensions, which explained 70.34 per cent of overall variance which is indicating that the variance of original values was captured by these three factors as shown in Table 4. The factors which have an eigenvalue more than one are only considered in this research. Eigenvalue or latent root is the sum of squared values of factor loadings relating to a factor (Krishnaswami & Ranganatham, 2007). The three factors are provisionally named information (IN), merchandise (MR) and convenience (CO). The index of levels of satisfaction on pre-purchase attributes with these three factors can be expressed as it is shown in Equation (2). Considering these three factors as variables, Equation (3) can be formed for further analysis by substituting these factors in Equation (1).

Table 4.

Factors: Pre-purchase Attributes

Factor	Eigenvalues	Total Variance %	Cumulative %
Information	3.295	35.62	35.62
Merchandise	2.358	22.36	57.98
Convenience	1.123	12.36	70.34

Source: Factor analysis data reduction (SPSS 16.0).

\begin{matrix} \Pr e ​ - ​ p u r c h a s e A t t r i b u t e s S a t i s f a c t i o n I n d e x (P R E) = f (I N F O R M A T I O N (I N), \\ M E R C H A N D I S E (M R), \\ C O N V E N I E N C E (O C)) \end{matrix}

(4)

P R E_{i} = f_{i 1} I N + f_{i 2} M R + f_{i 3} C O + U_{i}^{u}

(5)

Factor Scores Matrix: Pre-purchase Attributes

The factor scores matrix of pre-purchase attributes shows the associated variables in all the three factors and their relative factor scores as presented in Table 5. The first factor formed is INFORMATION with an eigenvalue of 3.295, variance of 35.62 per cent and five associated variables. The associated variables are availability-of-product-information (factor score 0.85), availability-of-pricing-information (0.81), information-retrieval-methods (0.77), access-to-information (0.68) and interactive-search-process (0.58). The second factor formed is MERCHANDISE with an eigenvalue of 2.358, variance of 22.36 per cent and two associated variables. The associated variables are array-of-product-alternatives (0.82) and range-of-products (0.69). The third factor formed is CONVENIENCE with an eigenvalue of 1.123, variance of 12.36 per cent and two associated variables. The associated variables are convenience-to-log-on (.072) and ease-of-use-of-store (0.56). One variable, minimum-transaction-cost, is eliminated while performing factor analysis with statistical package SPSS as the customers were not satisfied with transaction cost with e-tail outlet.

Table 5.

Factor Scores Matrix—Pre-purchase Attributes

Attributes	Information	Merchandise	Convenience
Availability of product information	0.85
Availability of pricing information	0.81
Information retrieval methods	0.77
Access to information	0.68
Interactive search process	0.58
Array of product alternatives		0.82
Range of products		0.69
Convenience to log on			0.72
Ease of use of e-store			0.56

Sources: Values from field data compiled by the author.

Note: Extraction method: Principal component analysis; Rotation method: Varimax with Kaiser normalization.

Levels of Customer Satisfaction towards Purchase Process Attributes (PUR)

In this section, an attempt has been made to analyze the levels of customer satisfaction towards attributes of purchase process provided by the e-tail website. Purchase process attributes refer to the attributes that provide convenience to customers while shopping online. The customers were asked to respond with their satisfactions on a five-point Likert scale on 20 variables identified from literature review. To determine the data reliability, reliability test was performed on the data of purchase process attributes. The value of the Cronbach’s alpha is found to be 0.795, which shows the data of purchase process attributes is 79.5 per cent reliable which ensures to proceed for further analysis.

Reliability of Data: Kaiser–Meyer–Olkin (KMO) and Bartlett’s Test for Purchase Process Attributes

To determine the appropriateness of application of factor analysis on the identified attributes of purchase process, KMO and Bartlett’s test was performed as shown in Table 6. The KMO measure is observed to be 0.652 which is higher than the threshold value of 0.5 (Hair et al., 1998). So, it can be interpreted that there is no error in 65.20 per cent of the sample and remaining 34.80 per cent, there may occur some sort of error. Bartlett’s test of sphericity (γ² = 6786.695) is found to be significant (p < 0.001, df = 190). Finally, it can be concluded that the data collected on purchase process attributes are appropriate for factor analysis.

Table 6.

KMO and Bartlett’s Test for Purchase Process Attributes

Kaiser–Meyer–Olkin Measure of Sampling Adequacy		0.652
Bartlett’s test of sphericity	Approx. chi-square	6786.695
	df	190
	Sig.	0.000

Source: Factor analysis data reduction.

Table 7.

Factors—Purchase Process Attributes

Factor	Eigenvalues	Total Variance %	Cumulative %
Transaction efforts	5.345	28.75	28.75
Availability	2.789	12.98	41.73
Security	2.114	11.63	53.36
Website design	1.354	7.66	61.02
Shopping experience	1.247	6.25	67.27
Check out time	1.102	5.45	72.72

Source: Factor analysis data reduction.

Factors: Purchase Process Attributes

After performing factor analysis, the twenty variables were reduced to six factor dimensions, which explained 72.72 per cent of overall variance which is indicating that the variance of original values was captured by these six factors as shown in Table 7. The six factors are provisionally named as Transaction Efforts (TE), Availability (AVL), Security (SC), Website Design (WB), Shopping Experience (SE) and Checkout time (CT).

Factor Scores Matrix: Purchase Process Attributes

\begin{matrix} P u r c h a s e \Pr o c e s s A t t r i b u t e s S a t i s f a c t i o n I n d e x (P U R) = f (T R A N S A C T I O N \\ E F F O R T S (T E), \\ A V A I L A B I L I T Y (A V L), \\ S E C U R I T Y (S C), \\ W E B S I T E D E S I G N (W B), \\ S H O P P I N G E X P E R I E N C E (S E), \\ C H E C K ​ O U T T I M E (C T)) \end{matrix}

(6)

P U R_{i} = f_{i 1} T E + f_{i 2} S H + f_{i 3} S C + f_{i 4} W B + f_{i 5} S E + f_{i 6} C T + U_{i}^{u}

(7)

The factor scores matrix of purchase process attributes presents the variables that are associated within each factor and their relative factor scores as represented in Table 8. The first factor, TRANSACTION EFFORTS, is formed with an eigenvalue of 5.345, variance of 28.75 per cent and four associated variables. The associated variables are period-of-filling-out-online-order-forms (0.90), assistance-to-the-user (0.87), speed-of-response-time-to-e-customers-queries (0.71) and limited-amount-of-effort-in-online-shopping (0.52). The second factor, AVAILABILITY, is formed with an eigenvalue of 2.789, variance of 12.98 per cent and four associated variables. The associated variables are availability-of-inventory (0.80), speed-of-shipping (0.70), online-tracking-of-customer-orders (0.63) and cost-of-shipping (0.51). The third factor, SECURITY, is formed with an eigenvalue of 2.114, variance of 11.63 per cent and three associated variables. The associated variables are payment-security (0.81), secure-ordering (0.71) and security-and-privacy (.51). The fourth factor, WEBSITE DESIGN, is formed with an eigenvalue of 1.354, variance of 7.66 per cent and four associated variables. The associated variables are screen-layout (0.81), efficient-website-design (0.67), use-of-colour-and-background (0.58) and page-composition (0.56). The fifth factor, SHOPPING EXPERIENCE, is formed with an eigenvalue of 1.247, variance of 6.25 per cent and three associated variables. The associated variables are ease-and-convenience-of-shopping (0.83), substitutability-of-personal-examination (0.73) and shopping-experience (0.65). The sixth factor, CHECKOUT TIME, is formed with an eigenvalue of 1.102, variance of 5.45 per cent and two associated variables. The associated variables are fast-checkout (0.76) and convenient-checkout (0.66).

Table 8.

Factor Scores Matrix—Purchase Process Attributes

Variables	Transaction Efforts	Availability	Security	Website Design	Shopping Experience	Checkout Time
Period of filling out online order forms	0.90
Assistance to the user	0.87
Speed of response time to e-customers queries	0.71
Limited amount of effort in online shopping	0.52
Availability of inventory		0.80
Speed of shipping		0.70
Online tracking of customer orders		0.63
Costs of shipping		0.51
Payment security			0.81
Secure ordering			0.71
Security and privacy			0.51
Screen layout				0.81
Efficient website design				0.67
Use of colour and background				0.58
Page composition				0.56
Ease and convenience of shopping					0.83
Substitutability of personal examination					0.73
Shopping experience					0.65
Fast checkout						0.76
Convenient checkout						0.66

Sources: Values from field data compiled by the author.

Note: Extraction method: Principal component analysis; Rotation method: Varimax with Kaiser normalization.

Levels of Customer Satisfaction towards Post-purchase Attributes (POS)

In this section, an attempt has been made to analyse the attributes of customer satisfaction in postpurchase stage. Post-purchase attributes refer to those attributes that make the customer satisfied even after purchase process and consumed the product. To determine the data reliability, reliability test was performed on the data of post-purchase attributes. The value of the Cronbach’s alpha is found to be 0.789, which shows the data of post-purchase-attributes is 78.9 per cent reliable which ensures to proceed for further analysis.

Reliability of Data: Kaiser–Meyer–Olkin (KMO) and Bartlett’s Test for Post-purchase Attributes

To determine the appropriateness of application of factor analysis on the identified attributes of post-purchase behaviour, KMO and Bartlett’s test was performed as shown in Table 9. The KMO measure is observed to be 0.695 which is higher than the threshold value of 0.5 (Hair et al., 1998). So, it can be interpreted that there is no error in 69.5 per cent of the sample and remaining 30.5 per cent, there may occur some sort of error. Bartlett’s test of sphericity (γ² = 4375.589) is found to be significant (p < 0.001, df = 105). Finally, it can be concluded that the data collected on post-purchase attributes are appropriate for factor analysis.

Table 9.

KMO and Bartlett’s Test for Post-purchase Attributes

Kaiser–Meyer–Olkin Measure of Sampling Adequacy		0.695
Bartlett’s test of sphericity	Approx. chi-square	4375.589
	df	105
	Sig.	0.000

Source: Factor analysis data reduction.

Factors: Post-purchase Attributes

After performing factor analysis, the 15 attributes were reduced to four factor dimensions, which explained 70.98 per cent of overall variance which is indicating that the variance of original values was captured by these four factors as shown in Table 10. The four factors are provisionally named Delivery (DLY), Return Policy (RP), Customer Services (CS) and Benefits (BT).

Table 10.

Factors—Post-purchase Attributes

Factor	Eigenvalues	Total Variance %	Cumulative %
Delivery	3.789	29.63	29.63
Return policy	3.145	20.41	50.04
Customer services	1.569	12.69	62.73
Benefits	1.112	8.25	70.98

Source: Factor analysis data reduction.

Factor Scores Matrix: Post-purchase Attributes

Table 11.

Factor Scores Matrix—Post-purchase Attributes

Variables	Delivery	Return Policy	Customer Services	Benefits
Delivery of merchandise as on time	0.93
Reliability of product delivery	0.82
Timeliness of product delivery	0.65
Product condition		0.72
Return and replacement process		0.70
Post-purchase communication on order refund requests		0.68
Channels to share customers’ experiences with others		0.62
Channels to resolve customer service issues		0.61
Feedback			0.79
After sales service			0.64
Customer support			0.51
Complimentary benefits				0.87
Loyalty programmes				0.81
Post-purchase communication				0.50

Source: Values from field data compiled by the author.

Note: Extraction method: Principal component analysis; Rotation method: Varimax with Kaiser normalization.

\begin{matrix} P o s t ​ - ​ p u r c h a s e A t t r i b u t e s S a t i s f a c t i o n I n d e x (P O S) = f (D E L I V E R Y (D L Y), \\ R E T U R N P O L I C Y (R P) \\ C U S T O M E R S E R V I C E S \\ (C S), B E N E F I T S (B T)) \end{matrix}

(8)

P O S_{i} = f_{i 1} D L Y + f_{i 2} R P + f_{i 3} C S + f_{i 4} B T + U_{i}^{u}

(9)

The factor scores matrix of post-purchase attributes presents the variables that are associated within each factor and their relative factor scores as represented in Table 11. The first factor, DELIVERY, is formed with an eigenvalue of 3.789, variance of 29.63 per cent and three associated variables. The associated variables are delivery-of-merchandise-on-time (0.93), reliability-of-product-delivery (0.82) and timeliness-of-product-delivery (0.65). The second factor, RETURN POLICY, is formed with an eigenvalue of 3.145, variance of 20.41 per cent and five associated variables. The associated variables are product-condition (0.72), return-and-replacement-process (0.70), post-purchase-communication-on-order-refund-requests (0.68), channels-to-share-customers’-experiences-with-others (0.62) and channelsto-resolve-customer-service-issues (0.61). The third factor, CUSTOMER SERVICES, is formed with an eigenvalue of 1.569, variance of 12.69 per cent and three associated variables. The associated variables are feedback (0.79), after-sales-service (0.64) and customer-support (0.51). The fourth factor, BENEFITS, is formed with an eigenvalue of 1.112, variance of 8.25 per cent and three associated variables. The associated variables are complimentary-benefits (0.87), loyalty-programmes (0.81) and post-purchase-communication (0.50). One attribute, greater-experience-of-online-shopping, is eliminated while performing factor analysis as the reason could be that the online customers are not having great shopping experience for their online purchase.

Index of Overall Customer Satisfaction on Attributes of Online Purchase (OCS)

In this section, an attempt has been made to identify overall customer satisfaction towards on identified pre-purchase attributes, purchase process attributes and post-purchase attributes. Considering these three variables as a factor, factor analysis was performed on data of customer satisfactions which are recorded on a five-point Likert scale (i.e., highly satisfied [5], satisfied [4], slightly satisfied [3], dissatisfied [2] and highly dissatisfied [1]). To determine the data reliability, reliability test was performed on the data of customer satisfaction on overall attributes. The value of the Cronbach’s alpha is found to be 0.655, which shows the data of overall attributes is 65.5 per cent reliable which ensures to proceed for further analysis.

Reliability of Data: Kaiser–Meyer–Olkin (KMO) and Bartlett’s Test for Overall Customer Satisfaction on Attributes

To determine the appropriateness of factor analysis on the levels of overall customer satisfaction on attributes, KMO and Bartlett’s test was performed as shown in Table 12. The KMO measure is observed to be 0.612 which is higher than the threshold value of 0.5 (Hair et al., 1998). So, it can be interpreted that there is no error in 61.2 per cent of the sample and remaining 38.8 per cent, there may occur some sort of error. Bartlett’s test of sphericity (γ² = 2874.695) is found to be significant (p < 0.001, df = 3). Finally, it can be concluded that the data of overall customer satisfaction on overall attributes are appropriate for factor analysis.

Table 12.

KMO and Bartlett’s Test for Current Satisfaction Levels

Kaiser–Meyer–Olkin Measure of Sampling Adequacy		0.612
Bartlett’s test of sphericity	Approx. chi-square	2874.695
	df	3
	Sig.	0.000

Source: Factor analysis data reduction.

Factors: Customer Satisfaction on Overall Attributes

The factor analysis was performed using principle component extraction method with varimax rotation. After performing factor analysis, the three variables were reduced to one factor dimension, which explained 70.12 per cent of overall variance which is indicating that the variance of original values was captured by the factor as shown in Table 13. The factor is provisionally named overall satisfaction.

Table 13.

Factors—Overall Current Satisfaction levels

Factor	Eigenvalues	Total Variance %	Cumulative %
Overall satisfaction	1.895	70.12	70.12

Source: Factor analysis data reduction

Factor Scores Matrix: Customer Satisfaction on Overall Attributes

The factor scores matrix of customer satisfaction presents the attributes that are associated together in each factor and their relative factor scores as represented in Table 14. The only one factor, OVERALL SATISFACTION, is formed with an eigenvalue of 1.895, variance of 70.12 per cent and three associated variables. The associated variables are purchase process attributes (factor score 0.82), pre-purchase attributes (0.75) and post-purchase attributes (0.65). It can be interpreted that the customers are more satisfied with purchase process attributes followed by pre-purchase-attributes and post-purchase-attributes.

Table 14.

Factor Scores Matrix—Overall Current Satisfaction Levels

Variables	Overall Satisfaction
Satisfaction levels—Purchase process attributes	0.82
Satisfaction levels—Pre-purchase attributes	0.75
Satisfaction levels—Post-purchase attributes	0.65

Source: Values from field data compiled by the author.

Note: Extraction method: Principal component analysis; Rotation method: Varimax with Kaiser normalization.

Model Testing and Discussion

In this section, an attempt has been made to analyze the associations among overall customer satisfaction and satisfactions on pre-purchase attributes, purchase process attributes and post-purchase attributes. The objective of proposed model is to observe the attributes which have greater impact on overall customer satisfaction. The overall customer satisfaction (OCS) is depending on the attributes of prepurchase stage (PRE), purchase process stage (PUR) and post-purchase stage (POS). Pre-purchase satisfaction index (PRE) is formed with three factors, that is, information (IN), merchandise (MR) and convenience (CO). Purchase process satisfaction index (PUR) is formed with six factors like transaction efforts (TE), AVAILABILITY (AVL), security (SC), website design (WB), shopping experience (SE) and checkout time (CT). Post-purchase satisfaction index (POS) is formed with the four factors like delivery (DLY), return policy (RP), customer services (CS) and benefits (BT). By substituting the factors formed so far from Equations (5), (7) and (9), the following equation of indexes can be formed.

\begin{matrix} f_{i 1} P R E + f_{i 2} P U R + f_{i 3} P O S + U_{i}^{u} = f_{i 1} I N + f_{i 2} M R + f_{i 3} C O + f_{i 1} T E + f_{i 2} A V L + f_{i 3} S C + f_{i 4} W B + \\ f_{i 5} S E + f_{i 6} C T + f_{i 1} D L Y + f_{i 2} R P + f_{i 3} C S + f_{i 4} B T + U_{i}^{u} \end{matrix}

(10)

\begin{matrix} I C S_{i} = f_{i 1} I N + f_{i 2} M R + f_{i 3} C O + f_{i 1} T E + f_{i 2} A V L + f_{i 3} S C + f_{i 4} W B + f_{i 5} S E + \\ f_{i 6} C T + f_{i 1} D L Y + f_{i 2} R P + f_{i 3} C S + f_{i 4} B T + U_{i}^{u} \end{matrix}

(11)

Considering OCS index as dependent variable and all other formed factors as independent variables, the specified model is tested using multiple regression analysis. In previous sections, it is found that the factor pre-purchase satisfaction attributes satisfaction index (PRE) accounted about 70.34 per cent variance, the factor purchase process satisfaction attributes (PUR) with 72.72 per cent variance and post-purchase satisfaction index (IPOS) is with 70.98 per cent variance, whereas the overall satisfaction index (OCS) explained about 70.12 per cent variance. Hence, it can be appropriate to expect good results from the multiple regression analysis. An attempt has been made to know the impact of PRE, PUR and POS on OCS using multiple regression models, whatever may be the exploratory power (i.e., adjusted R²) as shown in Table 15.

Table 15.

Model Summary

Model	R	R square	Adjusted R square	Std. error of the estimate
1	0.712	0.526	0.495	0.88952

Source: Values from field data compiled by the author.

From the results of the model, it is noted that the R-value found to be 71.2 per cent indicating that variables used to test the model have good association and adjusted R square is about 53 per cent which indicate that the independent variables (attributes) got considerable exploratory power in depending variable (over customer satisfaction) in the study area sample. Further, it can be concluded that the attributes currently provided by the e-tailers are not able to provide high shopping satisfaction to the customers. The relationship between the overall customer satisfaction, pre-purchase, purchase process and post-purchase satisfactions is significant at 95 per cent confidence level (p < 0.05). From F-value, it is noted that the exploratory power is statistically significant at 1 per cent level as shown in the Table 16. That means there is a significant relationship between OCS, PRE, PUR and POS. Hence, it can be concluded that the proposed model is statistically tested with good results.

Table 16.

ANOVA Table

Model		Sum of squares	df	Mean square	F	Sig.
1	Regression	256.522	13	19.456	36.6045	0.00
	Residual	378.689	586	0.7012
	Total	599	599

Source: Values from field data compiled by the author.

Table 17.

Associations among Customer Satisfaction Attributes

Type of Service	Attributes	Unstandardized Coefficients (Beta)	Std. Error	t	Sig.
	(Constant)	−3.46E−17	0.032	0.000	1.000
Pre-purchase attributes	Information	−0.080	0.032	−2.464	0.000*
	Merchandise	−0.194	0.036	−5.426	0.000*
	Convenience	0.018	0.035	0.512	0.621
Purchase process attributes	Transaction efforts	−0.085	0.045	−1.892	0.100
	Availability	0.510	0.048	10.623	0.000*
	Security	0.184	0.036	5.049	0.000*
	Website design	0.094	0.034	2.773	0.016
	Shopping experience	−0.018	0.034	−0.535	0.694
	Checkout time	−0.036	0.034	−1.055	0.466
Post-Purchase Attributes	Delivery	0.687	0.033	20.937	0.000*
	Return policy	0.452	0.035	13.043	0.000*
	Customer services	0.616	0.046	13.398	0.000*
	Benefits	0.191	0.050	3.855	0.036

Source: Values from field data compiled by the author.

Note: *Retail services are significant.

From the Table 17, it can be observed that out of thirteen attributes only seven attributes are found to be statistically significant, that is, information, merchandise, availability, security, delivery, return policy and customer services. It is found that the post-purchase services have high influence on customer satisfaction as per model. The post-purchase attributes—delivery 68.7 per cent, customising-services 61.6 per cent, return policy 45.2 per cent and benefits 19.1 per cent have good influence on customer satisfaction. The customers are satisfied with these attributes as they are having significant contribution to overall customer satisfaction. Among purchase process attributes, availability of merchandise has good influence (51 per cent) on e-shopping experience followed by security of transactions (18.4 per cent) and website design (9.4 per cent). Other purchase process attributes like transaction efforts, shopping experience and checkout time are negatively correlating with the depending variable. These variables need to be improvised as their contribution to overall satisfaction is not significant. Among pre-purchase services, convenience has positive influence (1.8 per cent) and information and merchandise are negatively correlating in the study area. The information required by the online consumers is not sufficiently supplied to the customers which is very crucial for purchase decision-making process. Another attribute, merchandise, is needed to be improvised by offering good choice and range of merchandise. The new final tested model is as follows.

\begin{matrix} I C S_{i} = - (3.46 E - 17) - 0.080 f_{i 1} I N - 0.194 f_{i 2} M R + 0.018 f_{i 3} C O - 0.085 f_{i 1} T E + \\ \begin{array}{l} 0.510 f_{i 2} A V L + 0.184 f_{i 3} S C + 0.094 f_{i 4} W B - 0.018 f_{i 5} S E - 0.036 f_{i 6} C T + \\ \begin{matrix} 0.687 f_{i 1} D L Y + 0.452 f_{i 2} R P + 0.616 f_{i 3} C S + 0.191 f_{i 4} B T + U_{i}^{u} \end{matrix} \end{array} \end{matrix}

(12)

Implications of the Study Based on Socio-economic Analysis of Online Customers

In this section, an attempt has been made to analyze the socio-economic characteristics of respondents as presented in Table 18. Out of total 600 sample respondents, 425 (70.83 per cent) are male, and 175 (29.17 per cent) are female. It can be observed that women are not habituated to the online shopping which is more convenient to them. Therefore, the online marketers/online retailers have to educate them in their advertisements and persuade them by explaining how the purchase process attributes are providing assistance and convenience while shopping online.

Basing on their age, the respondents are classified into five groups. Out of total sample 600, 35 (5.83 per cent) are teenagers (13–19 years), 389 (64.83 per cent) are from young age (20–30 years), 124 (20.67 per cent) are from early middle age (31–40 years), 37 (6.17 per cent) belong to late middle age (41–50 years) and 15 (2.50 per cent) are from old age (above 50 years). Most of the respondents are from 20 to 30 years of age; therefore, the online retailers have to procure the merchandise targeted at this age group. For the older age group, the post-purchase attributes provided by e-store have to be explained to them so as to motivate them for online purchase.

Based on occupation, the respondents are classified into three groups: unemployed/students 32 (5.33 per cent), employed 444 (74 per cent) and business people 124 (20.67 per cent). Most of the online shoppers in study are employees. Online marketers have to target their offerings towards them and have to identify proper media vehicles so as to reach their marketing communications.

Table 18.

Socio-economic Profile of Respondents

Variable	Categories of Variable	Frequency	%
Gender	Male	425	70.83
	Female	175	29.17
Age	13–19 years	35	5.83
	20–30 years	389	64.83
	31–40 years	124	20.67
	41–50 years	37	6.17
	above 50 years	15	2.50
Occupation	Unemployed/students	32	5.33
	Employed	444	74.00
	Business people	124	20.67
Education	Primary education	2	0.33
	Secondary education	15	2.50
	Higher secondary/Diploma/ITI	58	9.67
	Graduation (UG)	305	50.83
	Postgraduation (PG)	193	32.17
	Higher than PG	27	4.50
Income	Less than `15,000	89	14.83
	Between `15,000 and `30,000	245	40.83
	Between `30,000 and `50,000	114	19.00
	More than `50,000	152	25.33
Size of Family	Two	120	20.00
	Three	233	38.83
	Four	212	35.33
	Five	23	3.83
	Six	12	2.00

Source: Field data compiled by the author.

Basing on the education, the respondents are classified into six groups, 2 (0.33 per cent) respondents completed primary education, 15 (2.5 per cent) have secondary education, 58 (9.67 per cent) completed higher secondary education, 305 (50.83 per cent) are graduated, 193 (32.17 per cent) have postgraduation qualification and 27 (4.5 per cent) are higher postgraduates. Majority of the online shoppers are graduates and postgraduates. The e-tailers have to concentrate on this issue of less literacy and computer illiteracy while segmenting the customers and communicating with them.

Basing on the income levels, the respondents are classified into four groups. Eighty-nine (14.83 per cent) are having monthly income less than ₹15,000, 245 (40.83 per cent) have income between ₹15,000 and ₹30,000, 114 (19 per cent) have income between ₹30,000 and ₹50,000, and another 152 (25.33 per cent) respondents have income more than ₹50,000. Overall, 55 per cent of the respondents are having income less than ₹30,000 and 45 per cent are having income more than ₹30,000. The family size of respondents is also analyzed: 120 (20 per cent) have family size two, 233 (38.83 per cent) have size three, 212 (35.33 per cent) have family size four, 23 (3.83 per cent) have five and 12 (2 per cent) of respondents have size six. The online marketers have to consider number of family members and their income levels before drawing marketing and merchandising strategies.

Implications of the Study Based on Shopping Behaviour of Online Shoppers

In this section, the shopping behaviour of the respondents is analyzed as shown in the Table 19. The behaviour patterns of the online customers, such as online shopping frequency, preferred time of online shopping, amount spent in online shopping, source of information for purchase decision-making and Internet browser software used for online shopping, are studied. The objective of this analysis is to assist the online retailers in designing their business strategies and deliver better financial results. The online shopping behaviour patterns are as follows.

From the total sample 600, the frequency of online shopping is observed as daily 23 (3.83 per cent), weekly 96 (16 per cente), biweekly 48 (8 per cent), monthly 201 (33.5 per cent), bimonthly 12 (2 per cent) and 220 (36.67 per cent) of respondents are performing e-shopping as per requirement of goods. It can be analyzed that majority of the customers are shopping online monthly and as per requirement. Most preferred time for shopping is analyzed as first week of the month 25 (42.67 per cent), second week 108 (18 per cent), last week of the month 13 (2.17 per cent) and 223 (37.17 per cent) of the respondents perform online shopping is as per requirement of goods. The e-store marketers have to plan their marketing communications during the first week of the month as most of them are shopping online at that time. It is also found that a good number of customers are shopping online as per requirement so that the marketers have to promote the e-store basing the attributes, such as prompt delivery, in-time delivery, door delivery and availability of required merchandise.

Table 19.

Shopping Behaviour of Respondents

Variable	Categories of Variable	Frequency	%
Frequency of shopping	Daily	23	3.83
	Weekly	96	16.00
	Biweekly	48	8.00
	Monthly	201	33.50
	Bimonthly	12	2.00
	As per requirement	220	36.67
Most preferred time of shopping	First week of month	256	42.67
	Second week of month	108	18.00
	Last week of month	13	2.17
	As per requirement	223	37.17
Amount spent per month	Less than `1000	201	33.50
	Between `1000 and `5000	268	44.67
	Between `5000 and `10,000	80	13.33
	More than `10,000	51	8.50
Source of Information about e-tail site	Newspapers	157	26.17
	Television	225	37.50
	Radio	29	4.83
	Internet ads	89	14.83
	Social networking sites	100	16.67
Internet Browser used in online shopping	Google Chrome	193	32.17
	Internet Explorer	156	26.00
	Mozilla Firefox	95	15.83
	Opera	48	8.00
	Any thing	108	18.00

Source: Field data compiled by the author.

The amount spent per month for online shopping by the customers is also analyzed, out of the sample 600, 201 (33.5 per cent) are spending less than ₹1000 for online shopping, 268 (44.67 per cent) of the customers are spending an amount between ₹1000 and ₹5000, 80 (13.33 per cent) are spending an amount between ₹5000 and ₹10,000 and 51 (8.5 per cent) are spending an amount more than ₹10,000. Based on the income levels and amount spent per month, the e-store managers have to take merchandise assortment decisions.

The source of information for the customers regarding website is analyzed, newspapers as source for 157 (26.17 per cent) customers, television 225 (37.50 per cent), radio 29 (4.83 per cent), Internet ads 89 (14.83 per cent) and social networking sites as a source for 100 (16.67 per cent) respondents. The information regarding e-stores should be promoted through these sources to convert the e-store as a brand.

A browser is as software which acts as interface between customer and web world through which online customers are accessing the e-stores. An analysis was performed to find out which browsers are used by the online shoppers to perform online shopping. The objective behind this analysis can be identifying the possibilities to have a tie-up between the browser and e-store. The tie-up can be like setting the e-store website as a default homepage of the browser, putting a separate button or label of e-store on the browser menu bar, provision on pop-up window to open e-store, etc. In the study area, out of 600 respondents, 193 (32.17 per cent) are using Google Chrome as browser, Internet Explorer 156 (26 per cent), Mozilla Firefox 95 (15.83 per cent), Opera 48 (8 per cent) and remaining 108 (18 per cent) are using any browser for performing online shopping.

Conclusion

Finally to conclude the study, information about the e-store, information on merchandise and convenience of online transaction are pre-purchase determinants that are motivating the online consumers to give hits to an e-store. The transaction efforts, availability of merchandise, online payment security, website design, shopping experience and less checkout time are purchase process determinants that make the online consumers to log on to a particular e-tail website. Reliable delivery, return policy, customer services and benefits of shopping in a particular site are post-purchase determinants that give hits to an e-store. Online retailers and academic researchers are recommended to use the empirical model executed in this research to find out the most expected e-tail determinants by the online consumers in their respective markets/study areas.

Limitations and Scope for Further Research

Basing on the review of literature and interviews, the present study concentrates on 45 attributes of online shopping, but there may be more than 45 attributes depending on e-store. The study was organized in Visakhapatnam city only, the sample size may not be representing the study area properly as the chances of occurring sampling error are moderate to high. The same study can be organized across the world where e-tailing exists every part of the world. Further, the same research can be organized on sectors, such as banking, insurance, travel and tourism, in services, consumer durables, consumer electronics, automobiles, etc. in goods. Further, it is recommended to the conduct this research for individual manufacturers and service providers for their websites.

Footnotes

Acknowledgements

The author is grateful to the anonymous referees of the journal for their extremely useful suggestions to improve the quality of the article. Usual disclaimers apply.

Profile of the Study Area

(Source: Aponline.gov.in—Official government website of Visakhapatnam)

Visakhapatnam is one of the north coastal districts of Andhra Pradesh state of India, and it lies between 17°15′ and 18°32′ northern latitude and 18°54′ and 83°30′ in eastern longitude. The population of the district is 4.28 million as per 2011 census, and this constituted 5.0 per cent of the population of the state while the geographical area of the district is 11,161 km², which is only 4.1 per cent of the area of the state. Out of the total population, 2.140 million are males, and 2.147 million are females. The urban population is 3.53 million, whereas rural population is 1.301 million. The sex ratio is 1003 females per 1000 males. The district has density of population of 384/km². The literacy rate is 67.7 per cent in the district.

References

Anchaliya

Chitnis

Bapna

& Shrivastava

(Eds). (2012). Job satisfaction of bank employees—A comparative study of public and private sector. Ensisage 2020: Emerging Business Practices. New Delhi: Excel Publications.

Bakos

J.Y.

(1991). A strategic analysis of electronic marketplaces. MIS Quarterly, 15(9), 295–310.

Bernardi

R.A.

(1994). Validating research results when Cronbach’s alpha is below .70: A methodological procedure. Educational and Psychological Measurement, 54(3), 766–775.

Betts

(2001). Turning browsers into buyers. MIT Sloan Management Review, 42(2), 8–9.

Brown

(1987). Institutional change in retailing: A review and synthesis. European Journal of Marketing, 21, 5–36.

Burke

R.R.

(2002). Technology and the customer interface what consumers want in the physical and virtual store. Academy of Marketing Science Journal, 30(4), 411–432.

Cao

& Zhao

(2004). Evaluations of e-tailer’s delivery fulfillment: Implications of firm characteristics and buyer heterogeneity. Journal of Service Research, 6, 347–360.

Chen

& Dubinsky

A.J.

(2003). A conceptual model of perceived customer value in e-commerce: A preliminary investigation. Psychology & Marketing, 20(4), 323–347.

Cheung

C.M.K.

& Lee

M.K.O.

(2001). Trust in internet shopping: Instrument development and validation through classical and modem approaches. Journal of Global Information Management, 9(3), 23–45.

10.

Das

Guin

K.K.

& Datta

(2013). Impact of store personality antecedents on store personality dimensions: An empirical study of department retail brands, Global Business Review, 14(3), 471–486.

11.

Delone

H.W.

& McLean

R.E.

(2004). Measuring e-commerce success: Applying information systems success model. International Journal of Electronic Commerce, 9, 31–39.

12.

Doolin

Dillon

Thompson

& Corner

J.L.

(2005). Perceived risk, the internet shopping experience and online purchasing behaviour: A New Zealand perspective. Journal of Global Information Management, 13, 66–67.

13.

Dunne

P.M.

& Lusch

R.F.

(2005). Retailing. Mason: South-Western.

14.

Ellis

& Marino

(1992). A managerial approach for customer satisfaction and fulfillment of the marketing concept. Journal of Applied Business Research, 8(2), 42–47.

15.

Eroglu

S.A.

Machleit

K.A.

& Davis

L.M.

(2001). Atmospheric qualities of online retailing: A conceptual model and implications. Journal of Business Research, 54, 77–184.

16.

Forsytbe

S.M.

& Sbi

(2003). Consumer patronage and risk perceptions in the internet shopping. Journal of Business Research, 56(11), 867–875.

17.

Gale

B.T.

(1994). Managing customer value (pp. 15–30). New York: Free Press.

18.

Grewal

Iyer

G.R.

& Levy

(2002). The Internet retailing: Enablers, limiters and market consequences. Journal of Business Research, 57(7), 703–713.

19.

Gritzalis

& Gritzalis

(2001). A digital seal solution for deploying trust on commercial transactions. Information Management & Computer Security, 9(2), 71–79.

20.

Grunert

K.G.

& Ramus

(2005). Consumers’ willingness to buy food through the internet. British Food Journal, 107(6), 381–403.

21.

Guttman

R.H.

Moukas

A.G.

& Maes

(1998). Agents as mediators in electronic commerce. International Journal of Electronic Markets, 8, 7–22.

22.

Hair

J.F.

Anderson

R.E.

Tatham

R.L.

& Black

W.C.

(1998). Multivariate data analysis (pp. 15–45). New Jersey: Prentice Hall Inc.

23.

Hagel

& Armstrong

A.G.

(1997). Net gain: Expanding markets through virtual communities (pp. 15–16). Boston: Harvard Business School Press.

24.

Kiff

J.S.

(2000). The lean dealership: A vision for the future from hunting to farming. Marketing Intelligence & Planning, 18(3), 112–126.

25.

Kim

Lee

Han

& Lee

(2002). Businesses as buildings: Metrics for the architectural quality of internet businesses. Information Systems Research, 13, 239–254.

26.

Klein

L.R.

(1998). Evaluating the potential of interactive media through a new lens: Search versus experience goods. Journal of Business Research, 41(3), 195–203.

27.

Kolesar

M.B.

& Galbrait

R.W.

(2000). A service marketing perspective on e-retailing: implications for e-retailers and directions for further research. The Internet Research: Electronic Networking Applications and Policy, 10, 424–438.

28.

Krishnaswami

O.R.

& Ranganatham

(2007). Methodology of research in social sciences (pp. 338–339). Hyderabad: Himalaya Publishing House.

29.

Kerin

R.A.

Jain

& Howard

D.J.

(1992). Store shopping experience and consumer price-quality-value perceptions. Journal of Retailing, 68(4), 376–397.

30.

Milliman

R.E.

& Decker

P.J.

(1990). The use of post-purchase communications to reduce dissonance and improve direct marketing effectiveness. Journal of Business Communication, 27(2), 159–170.

31.

Misra

(2001). A conceptual framework for creating customer value in e-retailing in India. South Asian Journal of Management, 16(4), 127–147.

32.

Myerson

A.R.

(1998). CompUSA: Revising computer retailing. The New York Times.

33.

Liang

T.P.

& Huang

J.S.

(1998). An empirical study on consumer acceptance of products in electronic markets: A transaction cost model. Decision Support Systems, 24, 29–43.

34.

Peterson

R.A.

Balasubramanian

& Bronnenberg

B.J.

(1997). Exploring the implications of the Internet for consumer marketing. Journal of the Academy of Marketing Science, 25(4), 329–346.

35.

Perreault

W.D.

Jr. & McCarthy

E.J.

(2002). Basic marketing (pp. 56–59). Boston: The McGraw-Hill Companies, Inc.

36.

Rao

(1999). Developing an effective e-tailing strategy. International Journal of Electronic Markets, 9, 89–92.

37.

Ranganathan

& Ganapathy

(2002). Key dimensions of business-to-consumer web sites. Information & Management, 39(6), 457–465.

38.

Ratchford

B.T.

Talukdar

& Lee

M.S.

(2001). A model of consumer choice of the Internet as an information source. International Journal of Electronic Commerce, 5, 7–21.

39.

Reichheld

& Schefter

(2000). E-loyalty: Your secret weapon on the web. Harvard Business Review, 78(4), 105–113.

40.

Tractinsky

& Rao

V. Srinivasan

. (2001). Social dimensions of internet shopping: Theory-based arguments for web-store design. Human Systems Management, 20, 195–121.

41.

Wang

& Huamg

A.S.

(2002). An empirical study of the Internet store customer post-shopping satisfaction. Special Issues of Information Systems, 3, 632–638.

42.

Zeithaml

V.A.

(1988). Consumer perceptions of price, quality and value: A means-end model and synthesis of evidence. Journal of Marketing, 52, 2–22.

43.

Zeithaml

V.A.

(2002). Service excellent in electronic channels. Managing Service Quality, 12(3), 135–138.

44.

Zeithaml

V.A.

Parasuraman

& Malhorta

(2000). A conceptual framework for understanding e-service quality: Implications for future research and managerial practice. (Working Paper Series, Report 115). Cambridge, MA: Marketing Science Institute.

45.

Zhang

& von Dran

G.M.

(2000). Satisfiers and dis-satisfiers: A two-factor model for website design and evaluation. Journal of the American Society for Information Science, 5, 1253–1268.

46.

Zott

& Amit

(2001). Value creation in e-business. Strategic Management Journal, 22, 493–520.