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A Refereed Monthly International Journal of Management

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Editorial Board A Refereed Monthly International Journal of Management
Prof. B. P. Sharma
(Editor in Chief)
Prof. Mahima Birla
(Additional Editor in Chief)
Dr. Khushbu Agarwal
(Editor)
Ms. Asha Galundia
(Circulation Manager)

 Editorial Team

Dr. Devendra Shrimali
Dr. Dharmesh Motwani
 

AUTHORS

Dr. Vahid Reza Mirabi
Associate Professor,
Department of Business Management,
Central Tehran Branch, Islamic Azad University,
Tehran, Iran.
Dr. Abbas Saleh Ardestani
Associate Professor,
Department of Business Management,
Central Tehran Branch, Islamic Azad University,
Tehran, Iran.
Meghdad Farajpour Pirbasti
Ph.D student in Business Management
Marketing Orientation,
Department of Business Management,
Central Tehran Branch, Islamic Azad University,
Tehran, Iran.

Abstract

Today, cyberspace business is developing; these type of companies is looking forward to discover the factors (positive and negative both)
that affects the customers' purchase intention and as well as helps in
increasing the sales volume of products and offer better services to the
customers. The present research studies the relationship between
marketing strategy, perceived risk, trust and intention of purchasing in
cyberspace customers in Iran. This study is applicable in terms of
purpose and the research method is descriptive-survey. The sample consists of 400 customers of cyberspace companies located in Tehran,capital of Iran. The standard questionnaire was used for this purpose.
Modeling of structural equations and minor partial squares methods
were used to analyze the data. The results of this research showed that
marketing strategy has a negative effect on perceived risk. It was also
confirmed that there is thea negative impact of risk on customer
confidence. In the following, it was found that the trust has a positive impact on customers' purchase singintention.
Keywords: Perceived Risk, Customer Trust, Intent to Purchase,
Marketing Strategy, Cyberspace Companies

Introduction

Such websites have also grown enormously with the rapid growth of e-commerce and the trend of most businesses to the Internet and the use of websites for the supply and introduction of products. Many factors affect the views and trust of customers who enter the websites which are designed in the form of an online store, so that the customers get a sense of satisfaction and trust from all the service providers who are on these websites. They intent to buy from those stores with fully reviewing the products introduced therein, and the level of understanding of the quality of electronic services provided by the online stores. Today, it cannot be ignored the impact of information technology and the Internet, in particular on the lives of people. Today, the Internet plays an important role in people's lives and has created a profound transformation in individual and organizational life. Every day, people spend a lot of time on the Internet and go to various sites to get information about goods and compare the goods. Companies advertise and give information about their products on sites (Esfidani, 2006; page 20). The Internet and social media have affected consumers and how marketers interact. The Internet has distinctive features (Patterson et al., 1997), including the increase of the ability to save large amounts of information at various cyberspace, providing a powerful and inexpensive tool for searching, increasing the interaction and the ability to provide information on demand, the ability to provide a media in the exchange of services and improving the cost of deployment for customers (Winernan et al., 2013).The Internet is an important channel for marketing and advertising. The reason for this is the ability of the Internet to reduce costs and make people easy to access online services. Companies can also easily access a large number of users and communicate with them at a low cost (Logos, 2004). Since the commercialization of the Internet and the introduction of the global information network, it has been rapidly expanding the e-commerce programs and websites. The environmental, organizational, and technological factors that create a competitive environment have made organizations and customers face with a different environment than before. One of these cases, is how the purchasing process is carried out by people in the cyberspace environment, where it is difficult to trust compared to the traditional environment, and the purchasing process comes with difficulty. As Sanaie et al., have pointed out, despite the increasing use of the Internet in Iran, e-commerce and Internet shopping have not been sufficiently widespread and people are not very welcome to purchase the products and services online from institutions and organizations that provide facilities. Since one of the biggest obstacles to the development of online shopping is the lack of people's trust and unfamiliarity with the institutions active in this field with the mechanisms of confidence (Sanaie et al, 2010), it can be concluded that the lack of trust is one of the problems encountered in cyberspace.

Consumer’s behavior has always been one of the key issues in marketing. Understanding the cyberspace purchasing mechanism and consumer behavior in the online environment is one of the primary priorities of activity for the business companies which intend to expand their activities and participate in the cyberspace market (Nematpour and Mirfakhrodini, 2015).

E-commerce has grown rapidly and the growth is going on with the Internet. This rapid growth has been faced by both customers and companies with new situations. In fact, companies face with more difficult conditions for survival because of more intense competition and it has been increased the consumers’ chances to choose good. The importance of virtual stores as one of the marketing channels is increasing day by day with the advancement of IT in the marketing environment, (Taheri & Akbari, 2015).There is a growing need for knowledge, theory and models of the consumer behavior in the Internet due to the electronic commerce revolution, which is an essential aspect of customer relations and marketing strategy (Close and Kukar-Kini, 2010). Online shopping behavior requires more understanding (Herrero and San Martin, 2012) and therefore, more researches are needed (Mustler et al., 2014).As the several studies have pointed out, the key to long-term success for retailers is to provide the consumer confidence (Sah and Han, 2003; Paolo and Figenson, 2006; Woos et al., 2014). But in the end, it is negatively affected by the observed risks (Hang and Cha, 2013; Camarelsaman, 2007), which are related to the products (Inyer and Lee, 2000) and with Internet vendors (Jiang et al., 2008). Therefore, testing the risk factors that affect online shopping is important, while paying attention to purchasing online consumers needs further investigation.

 

Review of Literature

Online Marketing

The success in today’s competitive world requires a good marketing strategy, however, the complex market conditions, the introduction of new information technologies, continuous changes in competitive conditions, etc. has made the market impossible to decide on the type of appropriate strategy. There are so many influencing factors on marketing strategy, such as competitors and customers. In this regard, consumer behavior is one of the key factors that plays a very important role (Nikookar et al. 2009). In the meantime, online marketing and how to set up a suitable strategy are much more difficult. According to Cutler et al. (2010), online marketing is "company efforts to deliver products and services and create customer relationships through the Internet." Nowadays, a large number of businesses are online. The Internet connects people around the world, and companies and consumers are starting to interact with each other through the Internet. Today, a large number of companies are online to businesses (Cutler et al., 2010).Offline marketing deals only with passive clients; instead, online marketing targets individuals who are actively deciding which websites to visit and what information, marketing, or advertising to receive (For example, newsletters) (Cutler et al., 2010).

 

Marketing Strategies

The different types of strategies are categorized into total company strategies, business strategies, and task strategies. Marketing strategy is a tool that goals are achieved through it. These strategies address the question of how goals can be put into practice. The success of a marketing plan depends on the effectiveness of the marketing strategy. Strategy can be set for each of the marketing mix elements. In fact, marketing strategy involves different variables that the company can control it or adapt itself to non-controllable variables in order to achieve its goals appropriately (Hosseini et al. 2011).

Aker (2009) states that marketing strategy involves various operations such as positioning, pricing, distribution, and global strategies; achieving success requires a sustainable competitive advantage and its development requires a proper understanding of the target market and its requirements.

In online shopping, a variety of marketing channels is increasing, as the complexity of consumer purchasing behavior is increasing (Coaglan et al., 2001).Consumers tend to relocate among e-channels when purchasing products, mainly due to the high financial, security and functional risks of the Internet (Lee, 2009). As a result, electronic retailers set up their marketing strategies and focus on reducing product risks and Internet vendors (Chicooke and Fletcher, 2010; Chiu et al., 2011). However, it has not been yet explored the effect of marketing strategies on observed risks with regard to products and online channels (Papas, 2016).

 

Perceived Risks

The internet sites have developed in the last years. However, at the same time with the development of their using, concerns about the risk of using these sites also developed. Previous studies have shown that the members of these sites are subject to malware, spam, phishing, theft of information and content modification and may experience emotional distress or damage to reputation. It has been reported a large number of user accounts for members of Internet sites that have been attacked in the past (Schroeder, 2010).The privacy of site members has also always been a challenge, and when information is disclosed, they may be abused to harass, extort, and dishonor the individuals’ reputation (Row and Sachil, 2013).

Product elements that play an important role in consumer purchasing decisions are price and quality (Sanchez et al., 2006);in terms of price, with the increase in product value, the risks observed in purchasing product also increase (Dowling, 1999).The qualitative aspects of a product determine its value on the ultimate preference (Sanchez et al., 2006).Quality is associated with performance risk and involves a potential flaw in a product to meet performance or quality needs (Kiang et al., 2011).The literature on trust and risk is limited for some reason. First, these studies did not provide a clear perception of factors and risk factors and failed to identify and distinguish the underlying risk factors. Except for Shin, the most of the previous studies that have considered privacy, are still ignoring the risks and dangers of cybercrime attacks (Row and Susil, 2013).The risks of using the site lead to the members’ lack of confidence in the use of network services and subsequently reduce or not to use the site. New knowledge in this field will allow service providers to develop strategies and mechanisms that will lead to the development of using the site. Shin examined the impact of security and privacy on Internet sites and discovered their effects on admission patterns. Quinn theorized the potential impact of social influences and privacy concerns on acceptance of services by users. (Row and Susil, 2013).

Perceived risk brings the good or bad emotions that may affect beliefs, attitudes and behavioral intentions (Paolo 2003).Many studies have indicated that perceived risk is like a multi-dimensional structure that divide risk factors into sub-sections, they together represent the overall risk associated with the purchase of a product or service (Rois-Maffe et al., 2009).These include the risks of performance, time, privacy and financial. Cunningham et al. (2005) found that perceived risk has a negative effect on bookings and online purchasing the tickets of airline companies. Lopez and Molina (2008) and Giffen et al. (2003) also state that perceived risk affects customer behavior and tendency to purchase the Internet and as the perceived risk is higher, there is less likelihood a customer purchases online.

Intention to Purchase

Internet shopping is referred to as a set of motives, behaviors, mechanisms and processes through which the customer purchases the goods they need using electronic networks. Internet shopping is a growing phenomenon around the world, especially in countries where the necessary Internet infrastructure has been created (Chu et al., 2006).Internet shopping intent is an important variable that determines the final purchase behavior. It evaluates the criteria including the quality of the Internet base, the search for information and product evaluation by the customer (Pdar et al., 2009). In fact, the intention of Internet purchasing reflects the willingness of the customer to purchase through the Internet site. Past research has identified the stimulating factors of online purchasing intent. A sample of them is a study done by Laie and Liang (2000) and indicated that Customers have a tendency to purchase on the internet when the Internet sales site provides desirable functions such as product catalogs, search engine, shopping comparison, price list, electronic payment (Liang and Laie, 2002).

On the other hand, when there is a risk, the level of consumer confidence will be advised to the sources of information and recommendations and the reviewed cases will affect the final decision about their purchase (Wang & Chang, 2013). Because reducing the financial and financial performance risks will lead to potential purchases (Suwylak et al., 2011).In addition, the quality and quantity of information will have an impact on consumer purchasing intent (Park et al., 2007).At the moment, retailers do not just encourage consumers to use retail websites, but also on the consumer motivation to repeat their purchases through these channels (Chu et al., 2012).

Trust

Rousseau et al. (1998) define the trust as a psychological state, which is formed as a multifaceted structure of two cognitive and emotional dimensions (Johnson & Garrison, 2005).Sir Simkh et al. (2002) have defined the trust as the consumer expectations of the performance of the store, its products, and how much a customer can ensure the promise of the store. Of course, in marketing relationships, trust is a risky behavior since the transactions may not be based on the rightness. Trust in a relationship reduces dependency risks and depends on the level of uncertainty, the appropriateness of information for decision-making, the ability to predict decision outcomes, and the assurance of decision-making. Many studies, such as the ones done by Cheng and Lee (2001), Kritt et al. (2003), Flowin et al. (2005), Giffen et al. (2003), Jaronpa et al. (2000) concluded that the trust negatively affects perceived risk and intends to purchase. In the following, it is discussed the hypotheses and theoretical model of the research.

 

Conceptual Model of Research

Research hypotheses

  1. The product marketing strategy has a direct and negative impact on the product price risk.
  2. The product marketing strategy has a direct and negative impact on the product quality risk.
  3. The vendor marketing strategy has a direct and negative impact on the vendor's quality risk.
  4. The vendor marketing strategy has a direct and negative impact on the vendor's security risk.
  5. The product price risk has a direct and negative impact on consumer confidence in the product.
  6. The product quality risk has a direct and negative impact on consumer confidence in the product.
  7. The product quality risk has a direct and positive impact on product price risk.
  8. The product quality risk has a direct and positive impact on vendor quality risk.
  9. The vendor quality risk has a direct and negative effect on consumer confidence to the vendor.
  10. The vendor quality risk has a direct and negative effect on the vendor security risk.
  11. The vendor security risk has a direct and negative effect on consumer confidence to the vendor.
  12. Consumer confidence in the product has a direct and positive effect on purchasing intent.
  13. Consumer confidence to the vendor has a direct and positive effect on purchasing intent.

 

Research Methodology

The present study is the descriptive type in terms of the purpose. The statistical community of research includes online customers in Tehran. A total of 400 questionnaires were collected from respondents. A standard questionnaire was used to measure the variables of the research. The questionnaire of this research has been gathered in person. 40 people from the population were selected and a questionnaire was provided to them in order to determine the reliability of the questionnaires. Cronbach's alpha method was used to determine the reliability of the questionnaire and the alpha value was 0.89.Therefore, it can be said that the questionnaire has acceptable stability and reliability. In Table 1, the Cronbach's alpha coefficient is given for each variable.

 

Table 1: Cronbach's alpha coefficient

Variable

Number of questions

Cronbach's alpha

Product Marketing Strategy

1-5

0.78

Vendor marketing strategy

6-10

0.81

Product Price Risk

11-13

0.75

Product Quality Risk

14-16

0.77

Vendor quality risk

17-25

0.86

Vendor security risk

26-30

0.77

Consumer confidence in product

31-34

0.77

Consumer confidence in vendor

35-38

0.74

Purchasing intent

39-41

0.75

 

The questionnaire was provided by several experts and professors for measuring the content validity, and according to their opinions, content validity was assessed and necessary changes were made. Factor analysis test and SPSS software were used to assess the structure validity. The evaluation indices in this test are Kaiser-Mayer-Olkin (KMO) and Bartlett's significant level. The results of this test are presented in Table 2.

Table 2. The results of factor analysis for structure validity of the research variables

Variable

KMO criterion

Bartlett's criterion

Product Marketing Strategy

0.751

0.00

Vendor marketing strategy

0.762

0.00

Product Price Risk

0.775

0.00

Product Quality Risk

0.743

0.00

Vendor quality risk

0.758

0.00

Vendor security risk

0.743

0.00

Consumer confidence in product

0.744

0.00

Consumer confidence in vendor

0.793

0.00

Purchasing intent

0.728

0.00

 

Bartlett's significance level should be less than 5% in order to confirm the structure validity, and values of more than 50% are acceptable for Kaiser-Mayer-Olkin (KMO).The mentioned indices were measured for research variables and according to the obtained values, validity of the structure variables was confirmed. In this research, least minor squares method and Smart PLS software have been used to examine and test the conceptual model. Modeling structural equations help the researcher to test a theoretical model composed of various components, both in general and in a precise way (Naami and Mazhari, 2014).

Data Analysis

Demographic Results

Before analyzing statistical data, it is necessary to describe this datain order to better understand the nature of the community that has been studied and become more familiar with the research variables. In this study, 257 men (64.2%) and 143 women (35.8%) participated in the study.Other demographic data are presented in Table 3.

 

Table 3. The results of descriptive statistics of the research

Variable

Domain

Numbers

Percentage

Gender

Male

257

64.2

Female

143

35.8

Marital status

Married

231

57.78

Single

169

42.22

Education level

Under diploma

38

9.5

Diploma

62

15.5

Associate degree and Bachelor

212

53.00

 

Correlation coefficients and statistics of the research variables

The relationship among the research variables and correlation coefficients among them, mean and standard deviations of variables are presented in Table 4.Table 4 presents Pearson correlation coefficients for examining the relationship among the hidden variables in two to two. On the main diameter of this matrix, number 1 is placed, in such way that each variable is completely correlated with itself. The positive coefficient indicates a positive and direct relationship between two variables and the negative coefficient indicates a negative and significant relationship between two variables.

The last two columns of this table present descriptive indices, if the mean value is above 3, then it can be said that the variable is evaluated above the average from the respondents' view and if the mean value obtained for the sample is below 3, it can be said that the variable is evaluated below the average from the viewer's view. The findings indicate that there is a desirable satisfaction of all research variables among the respondents.

 

 

Table 4: Correlation coefficients among variables, mean and standard deviation

 

 

1

2

3

4

5

6

7

8

9

1

Product Marketing Strategy

1

 

 

 

 

 

 

 

 

2

Vendor marketing strategy

0.44

1

 

 

 

 

 

 

 

3

Product Price Risk

0.42

0.59

1

 

 

 

 

 

 

4

Product quality Risk

0.49

0.66

0.54

1

 

 

 

 

 

5

Vendor quality risk

0.81

0.77

0.75

0.78

1

 

 

 

 

6

Vendor security risk

0.61

0.77

0.55

0.73

0.64

1

 

 

 

7

Consumer confidence in product

0.59

0.71

0.59

0.58

0.62

0.66

1

 

 

8

Consumer confidence to vendor

0.85

0.54

0.73

0.69

0.74

0.73

0.72

1

 

9

Purchasing Intent

0.61

0.59

0.55

0.63

0.79

0.76

0.66

0.69

1

 

AVE

0.67

0.71

0.73

0.79

0.75

0.76

0.74

0.77

0.71

 

CR

0.81

0.79

0.80

0.75

0.81

0.76

0.73

0.74

0.73

 

MEAN

3.459

3.012

2.896

3.012

3.789

3.841

3.963

3.874

3.971

 

SD

0.995

0.957

1.058

1.057

0.825

0.743

0.723

0.851

0.865

 

Validation of the research model using confirmatory factor analysis and structural equations

Figure 2 and Figure 3 illustrate the model of structural equations in the estimation of standard coefficients. All variables in this model are classified in two hidden and explicit groups. The explicit (rectangles) or observed variables are directly measured by the researcher, while the unobserved (ellipse) variables are not measured directly; but they are deduced from the relationships or correlations among the measured variables. Latent variables represent a series of theoretical structures, such as abstract concepts that are not directly visible and are created and observed through other observed variables.

 

Figure 2. The main model of research in the form of significant coefficients (t-value)

 

Results of the research hypotheses

According to figures 2 and 3, the effect of the product marketing strategy on product price risk is -0.223 with a t-value of 6.248.The effect of product marketing strategy on product quality risk has a path coefficient of 0.527, with a t-value of 3.988.

Vendor marketing strategy for the vendor quality risk has a path coefficient of -0.253with t-value of -3.632.The vendor marketing strategy for vendor security risk has a path coefficient of -0.246 with a t-value of - 4.123.The effect of the product price risk on consumer confidence in the product has a path coefficient of -0.526 with a t-value of -3.406.The effect of the product quality risk on the product price risk has a path coefficient of 0.751 with a t-value of 1.978.This hypothesis has not been approved in view of the fact that the significant number has not been given in the specified range. A summary of the hypotheses is completely presented following table.

Table 5. Summary of research hypotheses

Hypothesis number

Hypothesis

Path coefficient

t-statistics

Significance level

Result of hypothesis

From

To

1

Product Marketing Strategy

Product Price Risk

-0.223

-6.248

<0.01

confirmed

2

Product Marketing Strategy

product quality Risk

-0.527

-3.998

<0.01

confirmed

3

Vendor marketing strategy

vendor quality Risk

-0.253

-3.632

<0.01

confirmed

4

Vendor marketing strategy

vendor security Risk

-0.246

-4.123

<0.01

confirmed

5

Product Price Risk

Consumer confidence in product

-0.229

-6.054

<0.05

confirmed

6

Product quality Risk

Consumer confidence in product

-0.526

-3.406

<0.01

confirmed

7

Product quality Risk

Product Price Risk

0..751

1.978

Non-confirmed

8

Product quality Risk

Vendor quality risk

0.100

1.652

Non-confirmed

9

Vendor quality risk

Consumer confidence to vendor

-0.553

-8.393

<0.01

confirmed

10

Vendor quality risk

Vendor security risk

-0.187

-1.497

Non-confirmed

11

Vendor quality risk

Consumer confidence to vendor

-0.426

-2.446

<0.05

confirmed

12

Consumer confidence to product

Purchasing intent

 

0.379

8.670

<0.01

confirmed

13

Consumer confidence to vendor

Purchasing intent

 

0.300

8.028

<0.01

confirmed

 

 

General Model Fitting

In the output of the PLS software for fitting the model, GOF index, multiplication square of two mean values of the shared and average coefficients is determined to be 0.01, 0.25 and 0.36, respectively, as strong, average and weak.In this study, its value was 0.22, which indicates the fitting of the model.

Conclusion

This research has been conducted among the online customers in Iran. By analyzing the collected questionnaires, it was determined that ten of the thirteen hypotheses were confirmed. In the first and second hypotheses, it was found that the product marketing strategy has a direct and negative impact on the price and product risk. This result is similar to the result of a study done by Papas (2016) that concluded as Internet companies design their marketing strategy more accurately, pricing risks and product quality goes opposite. Hajipour et al. (2012) also found that as the marketing strategy of the products increases, the marketing capability increases and the risk of products will be indirectly reduced. In the third and fourth hypotheses of the research, it was found that the vendor’s marketing strategy has a direct and negative effect on the vendor’s quality and security risk. The amount of path coefficient reflects the greater impact of strategy development on the perceived quality of cyber stores and reduces the risk for these stores. This result is similar to the results of the study done by Papas (2016), Naemi and Amini Sabegh (2016). The hypotheses of five and six of study examined the impact of price and product risk on consumer confidence in the product. The path coefficient of the negative impact of product price risk on consumer confidence was -0.229 and the impact of product quality risk on consumer confidence was -0.526.In Papas (2016) research, the path coefficient was -0.247 and -0.352, respectively. These results indicated the negative impact of risk on customer confidence. In this research, it has been reviewed the hypotheses of the negative impact of product quality risk on product price risk and vendor risk, which have not been confirmed. Papas (2016) has indicated the relationship among these variables in his research. This difference can be attributed to the demographic characteristics of environmental and statistical sample members. It suggests that other factors affecting product price risk, except the product risk, as well as other factors, affect the vendor quality risk, except the product quality risk. Understanding these factors requires another comprehensive study. The ninth hypothesis of the research indicated that the vendor quality has a negative impact on the consumer confidence in the vendor. This result is similar to the results of studies done by Papas (2016), Nahimi, Amini Sabegh (2016), Haddin et al. (2014), Bahrain Zadeh and Ziaee (2012).This result is in line with the research that indicates the importance of considering the risk in cyberspace; the actions of these companies should be aimed at reducing the risk to the stores in order to increase the trust of customers. The tenth hypothesis of this study, which examines the negative impact of vendor quality risk on vendor security risk, was not confirmed. This conclusion is not similar to the result of the study done by Papas (2016) paper; in the research, the positive relationship among the variables has been confirmed. Therefore, according to the results obtained, further research is required to investigate this hypothesis. An examination of the eleventh hypothesis has also indicated that the vendor quality risk has a negative impact on consumer confidence. This result is similar to the results of the studies done by Papas (2016), Naemi, Amini Sabegh (2016), Haddin et al. (2014).Therefore, it is suggested that these companies deliver and package the goods in accordance with customers 'demands and increase the customer confidence by reducing the risk. The thirteenth and twelfth hypotheses of the research, investigate the impact of consumer confidence on the product and the vendor, whose positive impact on the customer purchasing intent has been confirmed.  This hypothesis has been studied in many researches. Said Ardakani and Jahanbazi (2015) found that customer confidence in offline space also has an impact on their purchasing intent. In cyberspace, this result is similar to the results of the studies done by Sanayeieet al. (2010), Papas (2016), Ghazizadeh et al. (2011), Naemi and Amini Sabegh (2016), Kim and Park (2010), Yasmin (2011) and Mao (2010).Therefore, companies can increase the trust of their customers by doing something like providing quality services, electronic brands and consequently, increase their purchasing intent.

 

References

  • Ardakani S. Zanbasi N. (2105). Effect of store image on customer intent: Trust and perceived risk as moderating variables. Journal of New Marketing Research. 5 (2) .53-72
  • Hajipour B. Darziyan Azizi A. Shamsi Kushki S. (2012). Explaining product-market strategy and company marketing capabilities on market performance. Journal of Business Exploration. 4(7) .54-87
  • Sanayeie A. Afari A. Sobabpour B. (2010). Evaluating of the Effective Factors on the Trust of Online Customers and Their Impact on Internet Shopping Intention by Using Structural Equations. First International Management and Accounting Conference
  • Haddadian A. Bagheri Mashhadi A.H. Honarouj Bojadan F. (2014). Effective factors on accepting Internet shopping in the airline industry. Journal of Tourism Studies .9 (27) .23-46
  • Ghazizadeh M. Sardari A. Zandiyeh Z. Roshan Qasiasi R. (2011). Determining the factors associated with purchasing intent in e-commerce (case study: Raja passenger trains company). Journal of Business Strategies. (1) .100-120
  • Naemi M. former Amini Sabegh Z. (2016). The role of Web-based marketing strategy on the perceived risk and the trust of customers in online shopping. International testing of new horizons in management and accounting sciences, economics and entrepreneurship
  • Nikookar G. H. Divandary A. Ebrahimi A. Esfidani M. (2009). Consumer Behavior Pattern and Internet Marketing Strategies: Iranian Home Appliances. Commercial Management Journal .1 (2) .135-150
 
 

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