




































Asian Finance & Banking Review 

Vol. 5, No. 1; 2021 

ISSN 2576-1161    E-ISSN 2576-1188 

Published by CRIBFB, USA 

  

30 

IMPACT OF BUYER’S LENIENT POLICY OF RETURN AND 

TRUST ON INTERNET PURCHASE DECISION IN SUPPLY 

CHAIN SECTOR OF PAKISTAN 
 

 

Muhammad Ammad Ansari 

MBA in Supply Chain Management 

Karachi University Business School 

University of Karachi, Pakistan 

E-mail: ammadraees@gmail.com 

 

Dr. Sohaib Uz Zaman 

Assistant Professor 

Karachi University Business School 

 University of Karachi, Pakistan 

E-mail: sohaibuzzaman@uok.edu.pk 

 

 

ABSTRACT 

This article aims to investigate the factors that impact the internet purchase decision in supply 

chain sector of Pakistan because our mode of purchasing has been changed dramatically as a 

result of the Internet. An increasing number of individuals prefer online mode of shopping rather 

than in physical stores. Despite of these benefits there possess some cons of online purchasing 

like complicated returns, difficult websites, no sales assistance and lack of trust. In this article, 

impact of two independent variable i.e., lenient policy of return of buyer and trust of buyer was 

analyzed against the dependent variable internet decision of purchase. A questionnaire of 21 

questions was prepared. Data of 100 respondents was collected from buyers of Karachi. 

Reliability Analysis, ANOVA and regression analysis are performed using SPSS tool to find the 

results. Study concluded that lenient policy of return of buyer and trust of buyer has positive 

association with internet decision of purchase in Pakistan which means when buyers are 

provided with lenient policies for the return of goods for any reason and their trust in seller 

increases it will impact the decision of purchase positively. Thus, it will be beneficial in terms of 

chasing market competition and generating higher returns.    

 

Keywords: Lenient Policy of Returns, Customer Trust, Internet Purchase Decision, Regression 

Analysis, Supply Chain Sector, Pakistan.  

 

INTRODUCTION 

The Internet is a significant piece of every day and week by week exercises of people 

approaching it. It is an entryway to the universe of information. Internet buying and selling has 

gained a significant growth in Pakistan as well. Store's merchandise exchange was a significant 

factor in a buying choice. In this way, having a thoroughly examined merchandise exchange 

policy showed in your store is a critical way to keep your clients. A merchandise exchange is 

acceptable business for physical stores, and it's fundamental to working together on the web too. 

mailto:ammadraees@gmail.com


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31 

In contrast to physical stores, online shoppers don't see and hold the actual item before they get 

it. So, the online business dealers should guarantee that merchandise exchanges are reasonable 

and interesting to their clients. A brief and clear merchandise exchange gives shoppers a sense of 

safety that what they are purchasing is destined to be what it is addressed to be. Assuming a 

retailer doesn't give this assurance; shoppers frequently become dubious and try not to purchase 

the item. Many merchandise exchanges have restrictive arrangements which may become barrier 

in internet decision of purchase.   

Client trust is a proportional conduct to what they get. Trust comes when you 

comprehend your client's necessities, regard them, and proposition important assistance. 

Acquiring client's trust is significant not exclusively to make them faithful and return, yet in 

addition so they demand their companions work with you as well. Trust is an administration 

system seeing someone concerning trades. It is portrayed by vulnerability, weakness or reliance 

Grazioli and Jarvenpaa (2003). In early explores, scarcely any variables have been perceived to 

impact internet shopping practices with the assistance of brought together buyer situated 

exploration model. The significant forerunners to online shopper conduct were taken to be earlier 

web-based shopping experience and trust. Grazioli and Jarvenpaa (2003) upheld that client don't 

take part in web-based shopping because of absence of trust. 

A customer's propensity to acquire a certain product or service is referred to as purchase 

intention. Purchase intentions are a measure of a respondent's willingness to buy something or 

use a service. The factors studied in this research are lenient policy of return of buyer and trust of 

buyer with internet decision of purchase. 

 

LITERATURE REVIEW 

The impact of lenient return policies on product purchase decisions and subsequent returns is 

investigated in the study by Petersen and Kumar (2010). He discovered that lenient return 

policies are linked to greater purchase and also greater return rates. 

Lenient return policies, according to Wood (2001) and Wang (2009), boost purchasing 

without increasing returns. When compared to the restricted return policy, the lenient return 

policy increased buying but did not result in higher return rates as per Wood (2001). Similar 

results were reported by Wang (2009), who discovered that lenient return policies boosted initial 

purchase decision but not return rate. 

Janakiraman et al. (2015) discover that return leniency has a direct beneficial impact on 

purchase and return behaviors. The mediating processes that explain how returns interpret 

to purchase intentions, however, are less well understood, according to their meta-analysis. 

A clear and acceptable return policy, according to Rogers and Tibben-Lemke (2001), is one of 

the most essential instruments for attracting consumers. Consumers who have made purchases 

from an online vendor in the presence of a product return (returned experienced customers), 

according to Qureshi et al. (2009), would repurchase from such vendors. 

When it comes to internet shopping, trust is crucial. Consumers cannot personally verify 

the goods, which lead to trust difficulties in internet purchases. Consumer trust increases online 

purchasing decisions significantly accordingly Mahliza, Febrina. (2020). 

 

PROBLEM STATEMENT 

As per the preceding literature available it is clear that return policy and customer trust are severe 

points of concerns for seller. Due to great competition, it is very difficult to retain customer 

which can be only possible by providing beneficial service like good return policy so this will 



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32 

simultaneously develop customer trust in us. This will lead to greater return and customer will 

come again for repurchase.  

 

RESEARCH OBJECTIVE 

Research objective is to find if there is positive association among of lenient policy of return of 

buyer and trust of buyer with internet decision of purchase in supply chain sector of Pakistan 

 

SIGNIFICANCE OF STUDY 

This study is essential for the internet purchasing supply chain sector as a whole in order to 

examine supply chain policy of return dimensions i.e., supply chain return risks derived 

disruption factor which allows company get stick to one seller. As the internet purchasing supply 

chain sector as a whole is related to high frequency risk of return dimensions due to the high 

uncertainty in internet purchasing supply chain environment Therefore, examine trust of buyer 

mediates positive association among of lenient policy of return of buyer and perceived seller 

quality with internet decision of purchase in supply chain sector of Pakistan. 

 

RESEARCH METHODOLOGY 

 

Conceptual frame work 

 

 

 

 

 

 

 

 

 

Research Approach 

Explanatory research type is used for this research to examine positive association among of 

lenient policy of return of buyer and trust of buyer with internet decision of purchase in internet 

purchasing supply chain sector of Pakistan. We have used quantitative research approach to find 

the association among the dependent and independent variable. Data collected was changed in to 

numbers to quantified and tested empirically.  

 

Sample Size and Population 

The sample size, selected for this study is 100.  For this study the target population is internet 

shopper and buyers exist in Karachi so it was not difficult to obtain data.  

 

Statistical Techniques 

This research used the SPSS software and according to research it is appropriate to run reliability 

analysis, ANOVA and regression analysis. 

 

Questionnaire and Measurement Instrument 

There are 21 items selected in preparing questionnaire for the dependent and independent 

variables i.e., lenient policy of return of buyer, trust of buyer, and internet decision of purchase.  

Online Return 

Policy Leniency 

Consumer Trust 

Online Purchase 

Decision 



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Each, the variables carried 7 items and is adopted to examine the impact of lenient policy of 

return of buyer and trust of buyer on the internet decision of purchase. 

 

Hypothesis 

H1: There is positive association between lenient policy of return of buyer and internet decision 

of purchase. 

H2: There is positive association between trust of buyer and internet decision of purchase. 

 

DATA ANALYSIS AND INTERPRETATION 

 

Demographics 

Gender 

 Frequency Percent Valid Percent Cumulative Percent 

Valid 

Male 61 61.0 61.0 61.0 

Female 39 39.0 39.0 100.0 

Total 100 100.0 100.0  

 

It is to be observed out of 100, 61 participants were male which extracted the 61% of the data 

and there were 39 female participants which extracted the 39% of the data collected. 

 

Age 

 Frequency Percent Valid Percent Cumulative Percent 

Valid 

20-25 yrs 44 44.0 44.0 44.0 

26-30 yrs 32 32.0 32.0 76.0 

31-35 yrs 15 15.0 15.0 91.0 

36-40 years 5 5.0 5.0 96.0 

Over 50 4 4.0 4.0 100.0 

Total 100 100.0 100.0  

 

Among 100 participants 44 number of participants which extracts 44.0% are belong to the age 

group of 20-25 years, 32 number of participants which extracts 32% are belong to the age group 

of 26-30 years, 15 number of participants which extracts 15% are belong to the age group of 31-

35 years, 5 number of participants which extracts 5% are belong to the age group of 36-40 years, 

4 number of participants which extracts 4% are belong to the age group of Over 50 years. 

 

Education 

 Frequency Percent Valid Percent Cumulative Percent 

Valid 

Matric 9 9.0 9.0 9.0 

Intermediate 17 17.0 17.0 26.0 

Bachelors 38 38.0 38.0 64.0 

Masters 35 35.0 35.0 99.0 

Other 1 1.0 1.0 100.0 

Total 100 100.0 100.0  



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Out of 100 participants 9 (9.0%) of the participants are Metric, 17 (17.0%) of the participants are 

Intermediate, 38 (38.0%) of the participants are Bachelors, 35 (35.0%) of the participants are 

Masters, 1 (1.0%) of the participants are others. 

 

Marital Status 

 Frequency Percent Valid Percent Cumulative Percent 

Valid 

Married 34 34.0 34.0 34.0 

Unmarried 66 66.0 66.0 100.0 

Total 100 100.0 100.0  

 

Out of 100 participants 34 (34.0%) of the participants are married, 66 (66%) of the participants 

are unmarried. 

 

Reliability Analysis 

 Variable Cronbach’s Alpha No. of items 

ORPL Internet Policy of return Leniency 0.891 07 

CT Trust of buyer 0.915 07 

OPD Internet Decision of purchase 0.945 07 

 Over all 0.968 21 

 

The reliability of data obtained is checked through Cronbach's alpha test. Acceptable range for 

Cronbach's alpha is α of 0.6-0.7 indicates an acceptable level of reliability. The values for 

reliability test obtained are greater than 0.7. For Internet Policy of return Leniency value is 

0.891, for trust of buyer is 0.915 and for Internet Decision of purchase is 0.945 

 

Model Summary 

 

Model R R 

Square 

Adjusted 

R Square 

Std. Error 

of the 

Estimate 

Change Statistics Durbin-

Watson R Square 

Change 

F 

Change 

df1 df2 Sig. F 

Change 

1 .874a .764 .759 .46465 .764 156.582 2 97 .000 1.980 

a. Predictors: (Constant), CT, ORPL 

b. Dependent Variable: OPD 

 

Table shows that the Independent Variables online return policy leniency and customer trust 

explain 76.4% of variance in the online purchase decision as represented by R square. 

 

ANOVA 

 

Model Sum of Squares df Mean Square F Sig. 

1 

Regression 67.613 2 33.807 156.582 .000b 

Residual 20.943 97 .216   

Total 88.556 99    



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a. Dependent Variable: OPD 

b. Predictors: (Constant), CT, ORPL 

 

Table shows that our model is significant at α=.05, F (2, 97) =156.582 and p = .000 which is less 

than .05 

 

Coefficient table 
 

Model Unstandardized 

Coefficients 

Standardized 

Coefficients 

t Sig. 95.0% Confidence 

Interval for B 

Correlations 

B Std. 

Error 

Beta Lower 

Bound 

Upper 

Bound 

Zero-

order 

Partial Part 

1 

(Constant) .255 .186  1.372 .173 -.114 .624    

ORPL .457 .106 .426 4.317 .000 .247 .667 .840 .401 .213 

CT .507 .104 .479 4.861 .000 .300 .713 .847 .443 .240 

a. Dependent Variable: OPD 

 

The table shows that all the predicators are significantly correlated with dependent variable. 

Online return policy leniency is 0.457 that shows significantly positive impact as (sig >0.05) on 

internet decision of purchase. Secondly, customer trust is 0.507 that shows significantly positive 

impact as (sig >0.05) on online purchase decision. 

 

Regression model: 

The regression equation obtained is: 

Y  o  1x1  2x2 + ∈ 

Y  0.255  0.457x1  0.507x2 

 

Where 

Y = Dependent Variable online purchase decision OPD  

o = intercept  

1 and 2 = slope 

X1 and X4 = Independent Variables online return policy leniency ORPL and customer trust CT 

∈= Residual or error term   

 

CONCLUSION 

Conclusively, this research categorically confirmed the impact of the purchasing by internet 

retailer antecedents i.e., internet policy of return leniency and trust of buyer on internet decision 

of purchase. Evidently, the Pakistani purchasing by internet sector in order to investigate 

antecedents i.e., internet policy of return leniency, trust of buyer on internet decision of purchase 

factors, which allows buyers get stick to one internet retailing website. As the purchasing by 

internet sector business interested in high frequency purchasing by internet retailer antecedents 

due to the high competition. Therefore, this research investigates the impact by internet website’s 

policy of return antecedents in the context of Pakistan, which is valid and practically proved.  

 



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Where, threats of new entrant are increasing day by day. Already there is huge competition exist 

in this sector which getting worse competition for the sake of acquiring maximum buyer i.e., 

market share in this website retailing domain towards Pakistani purchasing by internet sector. 

For the sake of market share and new buyer attraction and increasing retention buyer rate it is 

necessary to provide best policy of return in the sector for respective buyer and then attaining 

trust of buyer which will lead to attract buyer to purchase again and generate higher returns. 

 

Hypothesis 
Accepted Or 

Rejected 

H1: There is positive association between lenient policy of return of buyer 

and internet decision of purchase. 
Accepted 

H2: There is positive association between trust of buyer and internet 

decision of purchase 
Accepted 

 

 

FUTURE RECOMMENDATION 

Future research can be on role of third-party ICT policy of return dimensions i.e., internet policy 

of return leniency, trust of buyer-on-buyer policy of return with significant mediating effect of 

buyer satisfaction with provided policy of return in local courier sector of Pakistan. Secondly, 

research can be on effect of intermediary’s policy of return factors i.e., speed, responsiveness and 

timelines policy of return on retailer policy of return with significant mediating effect of retailer 

satisfaction with provided policy of return in local retailing sector of Pakistan. Lastly, research 

can be on impact of third vendor managed inventory policy of return on buyer loyalty with 

significant mediating effect of buyer satisfaction in local restaurants sector of Pakistan. 

 

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Copyright for this article is retained by the author(s), with first publication rights granted to the 

journal. This is an open-access article distributed under the terms and conditions of the Creative 

Commons Attribution license (https://creativecommons.org/licenses/by/4.0) 

 


