Journal of Internet Banking and Commerce ISSN: 1204-5357
ISSN: 1204-5357
+44-175-271-2024Master of economics, Lahore, Pakistan
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Online Shopping; Self-service; Customer Value; Continued Use
Technological advancements have helped e-commerce industry to grow and present model like B2C (Business to Consumer) in which customers meet sellers to make a purchase through online interfaces. Internet is such a medium through which customers access online interfaces for transactions [1]. Such online transactions lie under the heading of Innovative Self Service Technology (ISST) that is such kind of technology allowing customers to conduct transactions on their own without the help of service professionals [2]. Examples of ISST in banking division are ATMs, e-banking etc. whereas in retail industries, online shopping is there.
Monsuwe [3] has defined online shopping as customer’s purchase behavior and website clicked for purchase reason. Online shopping under ISST has provided unique merits to both retailers and customers such as from customer point of view; it offers variety of products, saves travel cost, increases value and satisfies customer [4] and from retailer point of view, it increases market share [1].
Recent studies have shown that trend of online shopping is mounting in Pakistan. There are over 100 online shopping stores in Pakistan [5] which shows that a large number of customers are adopting technology (i.e. ISST) to conduct online shopping. Self-service online retail stores in Pakistan are OLX, Daraz.pk, Symbios.pk, Shophive.pk, Kaymu.pk and many others [6].
Many studies conducted in Pakistan have concentrated on acceptance factor of ISST and not on customer’s refusal to persist use of technology [7]. There may be several factors of not continuing a technology like complexity, risk, difficulties and fraud [8].
To make customers to continue use from an online store, under fierce competition, becomes extra exigent in the case of online shopping unless some value is provided to them. Such value helps in retention of customers as established by Ho [9] and Weinstein [10] that customer’s retention and intention to repurchase from an online store is affected by value created by that ISST.
This research finds out what drives a customer to continue online shopping by investigating the upshots of ISST on customer value and finally through this customer value, developing intention in customers to repurchase. The ISST constructs such as ease of use, usefulness, costs saved and navigation were determined after in depth literature review.
Firstly, literature review along with developed hypothesis and predicted model is served. Then, tactics for analysis and established results are described. Conclusion and limitations are presented at the last.
The purpose of this study is to find out the factors creating repurchase intentions by development of value for customers.
Research Questions
• Is navigation through the website a key determinant of value creation while shopping online and do this created value contributes in online repurchase intention?
• Is saving of cost and time perceived by customers as value generating and do this has effect on repurchase intent?
• Do customers perceive more value by finding website easy to use and is there any impact on re-buy intentions?
• Is being competent to fulfill the needs of customers a key determinant of value development? Can repurchase intentions be developed through this?
Progressions in technology have helped retail industries to offer new ways of shopping that they achieved through online interfaces that reduces cost, increases value and satisfies customer [4]. Customers are the key target of a retail store whose success is dependent on their purchases. Customers are likely to use online interfaces to do any kind of shopping when they admit the merits of online retail stores and the same is confirmed by Swinyard and Smith, that consumers prefer to shop online because they like products delivered at home. They want their purchases to be private [11] and they want faster service [4]. Additional studies also established attributes such as convenience, reduced checkout times, lower price of online products and internet experience as the reasons of why consumers shop online [12]. Preceding articles concluded that perceived benefits are the reasons of why customers shop online, however, this study will make a contribution by inclusion of customer value as an important factor that makes customers to buy online.
A situation, when customers’ needs are met with privileged services provided by online retail stores, leads to value creation for the customers.
Clarity about customer value is given as an interactive biased experience in which a consumer interacts with the products or service [13]. Customer value is created when needs are fulfilled as described by Overby [14] that a service value is admitted when a customer’s required objectives are realized, when something above need is offered and when there is uniqueness or innovation. Sheth [15] stated that customer value covers diverse dimensions like functional that is about the useful performance, emotional that describes what customers feel when exposed with products and epistemic that entails the realization of customer’ snooping nature.
All the required values of customers are provided to them through some innovative technology that comes under heading of Innovative Self Service Technology (ISST). ISST is such a novel medium of commercial transactions where a customer is not required to maintain any contact with the service provider.
Innovative Self Service Technology (ISST)
Different studies have focused on different attributes of ISST. Globerson and Maggard suggested a framework making a prediction that customer’s recognition of ISST is supported on aspects like convenience, self-will, money saved, self-icon, peril and self-fulfillment. Dabholkar [16] proposed a feature-based framework of five factors of ISST including delivery speed, convenience, control, pleasure and reliability. Curran and Meuter [17] confirmed that customer’s intent to continue using a service is affected by apparent usefulness.
Meuter [18] stated that customers are satisfied when ISSTs present ease of use, shun intrusion from service personnel, financial and time discounts and convenience, with a conclusion that ease in use of technology and time secured are vital elements affecting the acceptance of ISST. Childers explained features of interactive shopping types including usefulness, navigation and enjoyment while using service and found that usefulness and convenience are strong predictors of customer’s thoughts towards use of online retail stores.
Study conducted by Khalifa [7] incorporated two attributes of ISST i.e. usefulness and online shopping satisfaction, and further established the effects of these two independent variables on online repurchase intention. Their study deduced that the consequence of fulfillment on repurchases intent is braced by shopping habits and experience. Study of Ho and Ko [9] focused on traits of ISST including utility, cost element and willpower, and their combined result was transferred through value and readiness of consumers to online repurchase intention. They presented a conclusion that both Customer Value (CV) and Customer Readiness (CR) react positively between ISST and online repurchase intention.
Demirci and Kara [4] established the impact of seven Self-checkout factors (i.e. functionality, enjoyment, security, assurance, design, convenience and customization) on customer satisfaction and customer loyalty, and concluded that these factors contribute positively towards customer satisfaction. Clemes [8] considered ISST factors like, variety, price and convenience, and risk factors counting demographics, service quality. Their study reached a conclusion that both ISST factors and risk factors contribute towards customer’s adoption of online shopping.
Innovative SST provides Customer Value
As mentioned above, attributes of innovative SST, by meeting customer’s necessities, helps in providing value to them. The same is supported by Overby [14] and Ho and Ko [9] in words that a service value is admitted when a customer’s required objectives are realized, and such objectives are fulfilled through use of ISST.
Customer Value Contributes to Retention of Customers
Many researchers have concentrated on espousal of ISST and not the thought that customers might reject to discontinue using ISST [8,18,19]. Afterwards, studies conducted by Khalifa and Liu [7], Ho and Ko [9] focused on intentions of customers in continuing use of service. When customer’s value is created by use of ISST, they then engage in continue use, as stated by Zeithaml [20] and Lee and Allaway [19] that when customers accept merits of ISST, they will think about ISST in a positive manner and will indulge in potential use.
Repurchase can be measured as continued behavior to shop from same online store [7]. An ample study on continued purchase intention has been conducted by marketing researchers. Definition of repurchase intention is typically given by Copeland [21] as the intent to frequently buy a good or service in the long run. Making this definition unfit as it originated from marketing, Khalifa [7] stated repurchase as buying from a specific vendor by making re-use of online stores. They further captured repurchase as prolonged behavior i.e. persistent in buying from one online shop.
Scherer, Wünderlich [22] also argued that customers will continue use of ISST when it provides some privilege to them. When customers admit the value given by ISST, it builds a repurchase intention in them. The same is affirmed that perceived customer value significantly produces intent of repurchase thereby retaining customers [23]. Further it can also be established that when a customer develops an intention to purchase again from an online store, this fact contributes to retention of customers.
A study conducted by Ho and Ko [9] investigated the customer value and customer readiness as mediating factors between ISST attributes (i.e. ease of use, usefulness, costs saved and self-control) and online repurchase intention. With respect to their study following gap is found that:
• This study will include “navigation” as fourth important factor of ISST instead of “self-control”.
• This research applies on industry i.e. online shopping stores whereas their study focused on the case of internet banking.
• This paper will include target audience of Lahore, Pakistan whereas their study was conducted with the audience of Taiwan, so this paper is an empirical contribution in itself.
The study of Gong, Stump and Maddox paid attention on the combined effects of demographic factors and two ISST factors (i.e. ease of use and usefulness) on online shopping intents. Their research presented a conclusion that usefulness contributes more in online shopping intentions. Gap with this study is found as:
• This article will focus on repurchase intention as dependent variable
• This study will include cost element and navigation as important attributes of ISST.
Cheema [1] focused on impacts of three ISST factors (i.e. ease of use, usefulness and enjoyment) on online shopping intentions and presented a conclusion that enjoyment and ease of use factor contributes more in online shopping intentions. Gap with this study is found as:
• Their study targeted internet users of Pakistan, whereas this study will focus on online shoppers in Lahore only.
• This study will include “cost element” and “navigation” as important attributes of ISST.
• This article will focus on “repurchase intention” as dependent variable.
The study of Chaudary [5] focused on effects of different website factors and risk factors on online shopping intentions and presented a direct impact of variety and convenience and indirect impact of risk on online shopping intentions. Gap with this study is found that this article will focus on the mediating effect of customer value on online repurchase intention.
Danish, Ahmad, Ateeq, Ali and Humayon [24] proposed a study to measure factors contributing to repurchase intention in telecom industry and presented an end that satisfaction has major contribution in creating repurchase intention. This study also measures the factors generating repurchase intention but is unique in a way that it focuses on retail sector of Lahore.
This study’s hypothetical model is based on IS Continuance theory, given by Bhattacherjee [25] which focuses that choices to continue an IS resemble to customer’s intent to repurchase. Further, he confirmed that customer intention to repurchase is determined by how much they are satisfied and how useful an online store is. Further, research conducted by Khalifa and Liu [7] extended the IS continuance model, presented by Bhattacherjee, by adding the mediating and moderating role of online shopping habit on repurchase intention. This paper extends the continuance theory given by Bhattacherjee and Ho and Ko, by adding the mediating role of customer value on repurchase intention. Moreover, important attribute of ISST i.e. ease of use, cost element and navigation were also added along with usefulness to capture its effects on online repurchase intention through customer value (as mediating factor).
Ho and Ko [9] presented a structural model including factors like ease of use, usefulness, cost element and self-control as driving factors in creating value for the customers and leading them to continue use of online banking. This paper also extends the structural framework presented by Ho and Ko (2008), by adding important attribute of ISST i.e. navigation to capture the effects of ISST on repurchase intention (of online shopping) through customer value.
Based on vast examination of text on Innovative Self Service Technology (ISST), four attributes were extracted naming Ease of use, Usefulness, Costs Saved and Navigation for purpose of investigating repurchase intentions of customer while shopping online. Customer value is selected as mediating variable as it contributes most in creation of repurchase intention. Dependent variable is selected based on a saying that customer’s retention is a contributing factor towards customer’s intention to repurchase.
Ease of Use
ISST’s vital attribute is ease of use as defined by Ho and Ko [9] that a self-service counter provides a simple process and clear interface to ensure customer’s effective use. The complications of ISST may hinder the customer’s fluency of completing a task [1]. Ease of use can be recognized as effort saved while shopping online [8] and this saving of effort leads to creation of value for the customers. When customers can easily find a product, it leads to their retention and repurchase intention. Based on the importance of ISST, a hypothesis can be formulated that:
H1: Perceive ease of use of ISST has a significant effect on customer value.
Usefulness
Customer’s plans are dependent on how useful the technological interface is. When it comes to online shopping, Cheema [1] argued that customers think about usefulness as performance enhancer. Same is proved by results of different studies who established usefulness as main predictor of customer’s intentions to use a technology in the workplace [26].
When a customer can perform tasks expediently on an online store; he or she considers that online store as useful. Davis [26] described the same in words that a convenience offering store is perceived by a customer as useful.
Moreover, perceived usefulness of online stores creates value for the customers as supported by Meuter that online stores’ usefulness effects behavior of customers and creates value for customers as they include ISST in their lives and work. Based on above statement and the importance of usefulness, a hypothesis is formulated that:
H2: Perceived usefulness of ISST has a significant effect on customer value
Costs Saved
Instead of going to manual shopping stores, customers prefer to buy from online stores thus saving time and money cost which creates value for them. The cost is “offer” part of a good which negatively effects customer value and costs saved is “obtain” part of a good which positively effects customer value [12].
Perceived costs mainly include time and money costs that a customer spends during transaction as said by Khalifa [7] that while engaging in innovative self-service technology, the amount of money and time saved can be categorized as costs saved. Time is saved when customers are indulged in usage of self-service rather than full service option.
Money saved is an economic benefit and is the vital attribute for some customers. They buy from the same online stores when they perceive such stores cheaper than other online stores. Based on this, hypothesis can be formulated that:
H3: Perceived costs saved of ISST has a significant effect on customer value
Navigation
Navigation is a free move through the web store in search of information and product as defined by Hoffman and Novak that system navigation is described as an under control move through the shopping store relating retrieval ways allowing superior independence of selections. It also relates to webpage design that impacts online buying decisions of customers. Based on this, hypothesis can be formulated that:
H4: Perceived navigation of ISST has a significant effect on customer value
Customer Value and Online repurchase intention
When a value is realized by a customer, it helps in retention of customers by creating intention of repurchase. Same is affirmed that perceived customer value has a positive significant impact on repurchase intent and customers’ retention. Base on this, hypothesis is formulated below (Framework 1):
H5: Customer value has a significant effect on customers’ intention to repurchase.
Framework 1: Continued use of online shopping.
Measures
Quantitative research method was employed in order to quantify that which attributes of Innovative Self-Service Technology contribute to customer repurchase intention and to generalize results from a population i.e. online shopping stores in Lahore.
The survey was conducted through the distribution of personally administered structured questionnaires. From findings of literature review, the questionnaire of 26 items was developed (Appendix Table 1). Likert scale, ranging from 1=strongly disagree to 5=strongly agree was used.
Population, Sample and Data Collection
Population is consisted of people engaged in online shopping in Lahore. As it was not possible to reach all customers involved in shopping due to budget and time constraints, reference was used to reach the target audience that comes under technique “snow-ball sampling”. A total of 200 people were reached through reference out of which a number of 122 respondents were repliers who used to buy from online retail stores like Kamyu.pk, Daraz.pk, Symbios.pk, Vmart.pk and Shophive.com.
Missing value analysis (Figure 1) was done before doing any analysis and it was found that there are less than 2 percent missing values in the data (which is below 5%, a level where it is required to generate random numbers before moving for any analysis), so in case of this study random numbers were not generated and further analysis was done given subsequently.
Figure 1: Missing Value Analysis.
Demographic Statistics
Appendix Table 2 is providing information about profile of respondents. Majority of the shoppers 66 (54.5%) were females out of a total of 122 respondents whereas 55(45.5%) were male buyers. Majority 39(32.5%) of the people involved in shopping were youngsters and aged from 21 to 30 which is also confirmed by Cheema [1]. Respondents 51(42.5%) having a bachelor degree were majorly involved in shopping. Respondents 34(28.6%) having earnings between 40,000 to 60,000 were majorly involved in shopping. Majority of the respondents 39(32.3%) were involved in purchase of fashion accessories (Appendix Table 2). Major respondents 52(43%) were not sure of frequency of their online shopping as they replied with negation. 2 months’ purchases of 64 (53.8%) respondents were less than 10. Almost 39 (32.5%) respondents’ purchases of 2 months ranged in a price of <Rs. 10,000.
To know whether sample is good representation of population or not, Green gave a general rule i.e. Sample Size > Independent variables * 8+ 50. In our case, 122 respondents have been selected as sample that is greater than the sample size suggested by Green i.e. 4*8+50=82 (here 4 is the number of independent variables). The same sample size of 122 respondents is also used by Khalifa and Liu [7] in their study of online shopping.
Descriptive Statistics
Appendix Table 3 is showing descriptive summary of survey. Mean score of items vary from 3.27 to 3.79 whereas the standard deviations vary from .926 to 1.167. Out of 122 respondents, 107 answered the complete questions and did not miss any.
Reliability Analysis
To check the reliability of items of all construct, SPSS 20.0 was used and their Cronbach’s alpha is calculated, given in following Table 1. Alpha value of greater than and equal to o.5 is referred as reliable. This tool gives us values ranging with 0.704 to 0.840 which shows that each construct holds high reliability.
| Constructs | Valid N | Number of Items | Cronbach’s Alpha |
|---|---|---|---|
| Ease of Use | 119 | 04 | 0.723 |
| Usefulness | 115 | 05 | 0.790 |
| Costs Saved | 118 | 04 | 0.783 |
| Navigation | 118 | 04 | 0.704 |
| Customer Value | 119 | 06 | 0.836 |
| Online Repurchase Intention | 119 | 03 | 0.840 |
Table 1: Reliability Analysis.
Cronbach’s alpha’s high value of each construct depicts that we can consider that Construct for further analysis. Concisely, it is highly reliable to quantify a construct if its Cronbach’s value is high.
Validity Analysis
Table 2 confirms validity of items of a construct. KMO assumes that constructs must be orthogonal, Hinton said that a KMO value greater than 0.6 is considered to be adequate and an answer approaching to 1 is more wanted. It is clear from Table 2, each construct is having a KMO value greater than 0.6 that is well acceptable. Bartlett’s test assumes that items are correlated and it is clear from table that p-value of <0.05 means that questions are correlated and we can proceed with further analysis. According to Bhattacherjee [25] construct validity is also confirmed when factor loadings exceed 0.7. And the same is proved where all factors have loadings <0.7 (Appendix Table 3).
| Constructs | No. of Items | KMO Analysis | Bartlett’s Analysis χ2 | p- value |
|---|---|---|---|---|
| Ease of Use | 04 | 0.718 | 91.631 | 0.000 |
| Usefulness | 05 | 0.799 | 158.381 | 0.000 |
| Costs Saved | 04 | 0.751 | 132.737 | 0.000 |
| Navigation | 04 | 0.687 | 83.829 | 0.000 |
| Customer Value | 06 | 0.808 | 267.165 | 0.000 |
| Online Repurchase Intention | 03 | 0.723 | 141.976 | 0.000 |
Table 2: Validity Analysis.
After applying PCA, 1 component was extracted for each of the constructs. A construct’s component having eigenvalue >1 is entitled as principal component that is employed for future analysis. Table 3 shows eigenvalues for each construct and total variances i.e. percentage information covered by that component for items of a construct.
Table 3 shows that results fit the condition of construct validity (i.e. Eigenvalues ≥1) and discriminant validity that can be confirmed from appendix Table 3 (i.e. loadings ≥0.40). It can be established that the data collected from the instrument (i.e. questionnaire) is valid.
| Construct | Components | Initial Eigenvalues | ||
|---|---|---|---|---|
| Total | % of variance explained | Cumulative Variance | ||
| Ease of Use | Comp1 | 2.188 | 54.710 | 54.710 |
| Usefulness | Comp1 | 2.739 | 54.778 | 54.778 |
| Costs Saved | Comp1 | 2.437 | 60.919 | 60.919 |
| Navigation | Comp1 | 2.124 | 53.104 | 53.104 |
| Customer Value | Comp1 | 3.315 | 55.523 | 55.523 |
| Online Repurchase Intention | Comp1 | 2.276 | 75.852 | 75.852 |
Table 3: Eigenvalues and Variances.
Regression Analysis
Regression analysis is employed to estimate consequences of Independent, Dependent and Mediating variables. Table 3 shows the relation ISST factors with Customer value and their significance test. H1 predicts that ease of use will positively impact customer value. As shown in Table 3, the β-coefficient for the relation between ease of use and CV is significant and positive (β=0.589, t=7.780, p<0.001), sustaining H1. H2 is predicting that usefulness will have a positive impact on CV. The same hypothesis is supported with results provided in Table 4, i.e. the relation between both is certain and positive (β=0.563, t=7.204, p<0.001). H3 is making a prediction regarding positive relation between cost saved and CV. Results to test H3 (Table 4) are showing a certain positive relation between cost element and CV, thus supporting H3 (β=0.379, t=4.368, p<0.001).
| Hypothesized path | β co-efficient | t-value | Sig level |
|---|---|---|---|
| H1: Ease of Use → Customer value | 0.589 | 7.780 | 0.000 |
| H2: Usefulness → Customer value | 0.563 | 7.204 | 0.000 |
| H3: Cost save → Customer value | 0.379 | 4.368 | 0.000 |
| H4: Navigation → Customer value | 0.645 | 9.008 | 0.000 |
| H5: Customer value → Online Repurchase Intention | 0.620 | 8.482 | 0.000 |
Table 4: Direct Pathway Relation.
H4 is predicting a significant relation between navigation and CV. As shown in Table 4, the β-coefficient for the relation between navigation and CV is significant and positive (β=0.645, t=9.008, p<0.001), supporting H4. Last hypothesis i.e. H5 is regarding CV and online repurchase intention. Results show a certain and positive relation between both (β=0.620, t=8.482, p<0.001).
Results also show that navigation factor of ISST plays most part in Customer value i.e. β=0.639. Whereas costs saved contributes least to customer value (β=0.397, p<0.001).
Mediation Analysis
Mediation can only be applied or tested when direct effects of variables with each other are significant. To run mediation, both independent and mediating variables are entered as independent variables.
Multiple regression analysis is being applied to examine the intervention of projected mediating variable between independent (ISST characteristics) and dependent variable (online repurchase intent). From Table 4, the direct effects of ISST factors on customer value (mediating variable) and of customer value on online repurchase intention (dependent variable) are significant. Table 5 shows that the direct effect of ISST factors on online repurchase intention are also significant thus, confirming the conditions of mediation.
| ISST Factors (Independent variable) | ORI (Dependent variable) |
|---|---|
| EOU | 0.579* (7.622) |
| USF | 0.725* (11.098) |
| CS | 0.353* (4.025) |
| NVG | 0.479* (5.845) |
*Significance <0.01, t-values are in parenthesis, EOU: Ease of use, USF: Usefulness, CS: Costs Saved, NVG: Navigation.
Table 5: Direct effects of ISST factors on Online Repurchase Intention.
Table 6 signifies the findings of multiple regression analysis where Customer value (intervening variable) was used in conjunction with traits of ISST as an independent variable. Outcomes illustrate that some effect of both factors of ISST i.e. EOU and USF is transferred to ORI through customer value and some effect is directly conveyed to ORI whereas rest factors of ISST i.e. CS and NVG are transferring all their effects through customer value, no direct effect to ORI is observed. Conclusion can also be drawn that mediation of CV is strong between CS and ORI (i.e. β=0.598), and between NVG and ORI (i.e. β=0.548).
| Independent variables | Dependent variable (Online Repurchase Intention) | |||
|---|---|---|---|---|
| CV | 0.438* (5.04) | 0.281* (3.71) | 0.598* (7.57) | 0.548* (5.59) |
| EOU | 0.325* (3.75) | |||
| USF | 0.555* (7.43) | |||
| CS | 0.128 (1.56) | |||
| NVG | 0.141 (1.46) | |||
| R | 0.677 | 0.761 | 0.650 | 0.633 |
| R2 | 0.458 | 0.579 | 0.423 | 0.401 |
| Adjusted R2 | 0.449 | 0.571 | 0.413 | 0.390 |
| F-statistics | 47.4* | 74.9* | 40.7* | 37.5* |
| Df | 112 | 111 | 113 | 114 |
| Sig-Level | 0 | 0 | 0 | 0 |
*Significance <0.001, t-values are in parenthesis, CV: Customer Value, EOU: Ease of use, USF: Usefulness, CS: Costs Saved, NVG: Navigation
Table 6: Multiple Regression Analysis for Mediation
R (the square root of R2) in Table 6 is indicating the correlation between observed and predicted values of the ORI. Adjusted R2 shows that by how much percentage dependent variable i.e. ORI is affected by joint effects of independent variables i.e. ISST factors and mediating variable i.e. CV. Table 6 shows that ORI is mostly affected by USF and CV whereas least affected by NVG and CV. Here F-values are investigating whether ISST factors and CV can forecast ORI significantly that will be confirmed only when sig-level is below .001. An asterisk sign with f values (Table 6) is confirming a significant impact.
There is a saying that when an online retail store succeeds in developing intentions to repurchase in customers, this then leads to customers’ retention. Retail stores are always looking for ways to retain customers. This article gives you a way, especially for managers, to determine what attributes of ISST are perceived by customers as value generating. This important finding can help managers to structure their technologies around the discussed factors including EOU, USF, CS and NVG with more emphasis on traits NVG and EOU.
Based on the results of this study, promotion of online shopping requires retail stores to address ISST and CV that affected the intention to re-purchase from online retail stores. Such stores should design a user-friendly interface for delivery of their services to prove the feature i.e. ease of use. Customers confirm the usefulness of the technology when they can conduct transactions effortlessly. Technology based service encounters offer cost benefits (thus saving labor costs) to the industry [16]. In addition, retail stores can also offer discounts on products (that customers buy online) and on service charges. Such benefits in terms of cost hyped as economic value could enhance customer’s eagerness to re-buy from online retail stores [9].
ISSTs play an important role in consumers’ routine. One of an important ISST is online shopping. Trend of online shopping in Pakistan is booming in different parts of the country that resulted in a shift of almost 50% of the customers from traditional shopping to online shopping. In such a situation, it becomes vital to know what makes customers to buy online, a need, a value, a benefit or a privilege. So it becomes essential to investigate those ISST factors that help in creation of value for the customers in order to create repurchase intentions.
This research has reached to certain results. According to study of Ho and Ko [9], ease of use, cost element and usefulness played a significant role in value creation which supports H1, H2 and H3. Their results are also verified by this study. But this paper finds the two factors of ISST i.e. navigation and ease of use to be mostly affecting the customer value. If the customer finds a website easy to use for a transaction, he will perceive more value from it. Contrary to a belief that cost factor of ISST is important in creation of value however, this study finds that customers rate cost factor as third important in value creation. Usefulness was also preferred by customers while shopping online.
According to Childers navigation of website plays a vital role in determining behavior of customers, which supports H4 formulated in this study. Same results are provided by this study in words that navigation factor contributes most in value creation for the customer which in turn leads to repurchase intention of customers.
CV establishes its strong influence on continued intentions which supports H5 [9,23] and this study clearly confirms that, CV has the significant and positive association with the repurchase intention of customers. Relating to results of this research, for retailers with ISST attributes, CV is illustrative aspect affecting customer’s intentions to continue use of online shopping.
Constraints and Future Study
To investigate the approach of Pakistani customers towards online shopping, several matters are still to be thought. First, as target audience was hidden, so snow ball sampling was used for data gathering, future research could use other sampling techniques like stratified sampling to draw more wide conclusions.
Second, the study focused only on the customers indulged in online shopping and paid no attention to nonusers. A future study may include attitudes of both users and nonusers about online shopping. Sample was constrained to an explicit geographical region i.e. Lahore, this may have affected the findings; a future research could be a thorough study on online shopping in different divisions of Pakistan.
This study has captured the effect of positive ISST attributes towards customer value and repurchases intention; a future research can be done on negative factors like risk etc. Finally, while we have restricted ourselves to the working of Pakistani ISST websites, the study may incorporate working of other countries ISST websites operational in Pakistan.
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