Online repurchase Intention: empirical study on the household equipment market

Model development based on the concepts of technology adoption, reinforcement expectations and information systems duration. Use of the website and internet services to make purchases of household appliances. Confirmation of online shopping expectations.

Рубрика Маркетинг, реклама и торговля
Вид статья
Язык английский
Дата добавления 09.08.2021
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20

Rennes School of Business

Online repurchase intention: empirical study on the household equipment market

G. Videlaine, L. Scaringella

France

Abstract

The explosion of e-commerce has enabled consumers to purchase goods online. We have developed a conceptual framework based on the technology acceptance model, the expectation-confirmation theory and the concept of information systems continuance. Our intent is to conduct an empirical study on consumers' online repurchase of household equipment, which has not been undertaken before. 218 respondents answered our survey on the intention of online repurchase of IKEA products. Structural equation modeling has been used for the analysis. Concerning the household equipment market, our findings indicate that past Internet shopping experience affects online customers' perceived ease of use, confirmation and satisfaction. The usability of a website affects trust, perceived enjoyment and usefulness. Customers' confirmation affects satisfaction, but not perceived usefulness. Intention to repurchase household equipment online is positively affected by perceived usefulness and satisfaction. Analysis of our data leads to the counter-intuitive finding that online repurchase intention is negatively affected by perceived enjoyment. Our study contributes to bring together several extant online repurchase intention models.

Keywords: Internet, e-commerce, repurchase, household equipment, online shopping, IKEA.

Introduction

E-commerce has significantly changed the way consumers buy their products and services and offers advantages over shopping in a physical store [Wen, Prybutok, Xu, 2011].

The Internet has become an important marketing channel and an integral part of companies' strategy [Bart et al., 2005]. The Internet plays a strategic role in today's digital economy and facilitates new initiatives [Korzaan, Rutner, 2013].

M. Korzaan and P. Rutner argue that e-commerce “is growing faster than almost all other industries” and enables companies to generate revenue rapidly [Korzaan, Rutner, 2013, p. 70].

The fast growth of competition encourages companies to develop differentiation and enhance customer loyalty.

Searching for new customers is important, however, reinforcing customer loyalty is considered even more profitable, because the cost of retaining customers is lower than that of finding new ones [Wen, Prybutok, Xu, 2011].

A. Bhattacherjee argues that “acquiring new customers may cost as much as five times more than retaining existing ones” [Bhattacherjee, 2001, p. 352]. Understanding the factors that influence consumers' online repurchase intention, is central to our research.

Several extant papers [Oliver, 1980; Davis, 1989; Wen, Prybutok, Xu, 2011; Chen, 2012] contributed to the understanding of consumers' online purchase and repurchase behavior. The technology acceptance model (TAM) defined by F. Davis has explained consumers' intentions of using information technology [Davis, 1989]. The expectation-confirmation model (ECM) defined by Oliver was more focused on consumers' intentions of reusing the technology [Oliver, 1980].

Some more recent models have concentrated more specifically on online repurchase intention. Wen, Prybutok, Xu's model defined in 2011 reused the TAM and the ECM to validate the hypotheses concerning online repurchase intention [Wen, Prybutok, Xu, 2011]. Chen's model defined in 2012 also used the TAM and the ECM, as well as two other extant theories: the social cognitive theory and the social learning theory, which help to conceptualize the antecedents that influence consumers' online repurchase intentions [Chen, 2012]. Some studies have already been carried out in specific sectors to understand consumers' decision to repurchase online. Among those sectors are apparel websites [Tong, 2010; Lee, Hodges, 2012], tourism websites [Kim et al., 2014], the B2C market [Zhang et al., 2011] and luxury products [Ciornea, 2013]. However, no studies have been conducted on the household equipment market. The household equipment market comprises the following products: furniture, household electrical goods, home textile, decoration, lights and tableware. The leaders on the online household equipment market are: BUT, IKEA, Conforama, Darty, Boulanger, Desforges, Zara Home.

Our study addresses the following research question: what are the factors that influence consumers in their decision to repurchase household equipment online?

In the past years, different studies have been conducted about consumers' online purchase and repurchase behavior.

However, we observe that the current literature remains very separated in the sense that previous works [Oliver, 1980; Davis, 1989; Wen, Prybutok, Xu, 2011; Chen, 2012] remain very disconnected from each other, while we would learn more by having an integrated framework that embrace the existing studies.

Our intended contribution is to bring together four streams of research [Oliver, 1980; Davis, 1989; Wen, Prybutok, Xu, 2011; Chen, 2012], which was missing in the existing literature. Consequently, our contribution is bringing convergence to the existing studies.

We also aim at filling an empirical research gap by studying the specific case of the household equipment market to complement the existing studies dealing with apparel websites, tourism websites, the B2C market and luxury products. In the remainer of this paper, we provide a critical review of existing models and theories (TAM, ECM, Wen, Prybutok, Xu's model and Chen's model). Then, based on existing theories, we develop our hypotheses and the analytical framework for studying the household equipment market.

After that, we present the methodology we have used in our empirical study. After performing the analysis using structural equation modeling, based on our empirical results, we provide the results discussion and their con- triubtion to the literature.

1. Literature review

In this literature review, we examine more specifically four models. We have considered both the TAM and ECM models as the theoretical basis for our study as well as the Wen, Prybutok, Xu's and Chen's models that are more relevant for our research aim.

Table 1

Hypothesis tested in the expectation-confirmation model

Hypothesis

Result

H1. Users' level of satisfaction with initial information systems use is positively associated with their information systems continuance intention.

Supported

H2. Users' extent of confirmation is positively associated with their satisfaction with information systems use.

Supported

H3. Users' perceived usefulness of information systems use is positively associated with their satisfaction with information systems use.

Supported

H4. Users' information systems continuance intention is positively associated with their perceived usefulness of information systems use.

Supported

H5. Users' extent of confirmation is positively associated with their perceived usefulness of information systems use.

Supported

The Technology Acceptance Model. The purpose of the TAM developed by Davis and co-authors is to explain consumers' behavioral intention to use a system [Davis, 1989; Davis, Bagozzi, Warshaw, 1989]. The authors argued that the usefulness and user-friendliness of a technology positively influence a consumer's decision to use it again. Similar findings have been reported in the specific case of online purchase [Wen, Prybutok, Xu, 2011; Chen, 2012].

Widely acknowledged in this field of study, this model focuses on factors such as perceived usefulness and perceived user-friendliness and consequently does not take into account other possible determinants of behavioral intention to use a technology, such as trust or satisfaction. The models look at consumers' intention to use a technology or their initial decision to purchase it and neither addresses the question of reuse, nor that of repurchase. This is why we decided to use Bhattacherjee's ECM [Bhattacherjee, 2001] in conjunction with the TAM. The Expectation-confirmation Model and the Information Systems continuance. In the information systems (IS) continuance model, A. Bhattacherjee argues that consumers' satisfaction is a function of expectation, perceived performance and confirmation, which encourages consumers to repeat online purchases [Bhattacherjee, 2001].

This model was based on the ECM [Oliver, 1980], which evaluates consumers' satisfaction, post-purchase behavior and the marketing service in general. With the revised information systems continuance model, A. Bhattacherjee argues that consumers' information systems continuance intention is primarily determined by satisfaction and perceived usefulness [Bhattacherjee, 2001]. User satisfaction is determined by perceived usefulness and confirmation (expectations confirmed), and perceived usefulness is influenced by users' confirmation.

However, this model does not take into account other important factors such as trust, perceived ease of use and perceived enjoyment. In the Table 1, we are providing the summaries of the hypothesizes tested in the ECM.

Wen, Prybutok, Xu's Model. Based on [Davis, 1989] and [Bhattacherjee, 2001], Wen, Prybutok and Xu developed a specific model for e-commerce [Wen, Pry- butok, Xu, 2011].

This model is one of the best suited for understanding the motives behind customers' decision to return and repurchase goods from a household equipment website because it takes into account almost all the constructs we need to evaluate and test in our study. The authors validated Davis' and Bhattacherjee's hypotheses [Davis, 1989; Bhattacherjee, 2001].

Table 2

Hypothesis tested in the Wen, Prybutok, Xu's model

Hypothesis

Result

H1: A customer's perceived ease of use of an e-commercial website positively affects a customer's perceived usefulness.

Supported

H2: A customer's perceived usefulness of an e-commercial website positively affects a customer's online repurchase intention.

Supported

H3: A customer's confirmation of an e-commercial website positively affects a customer's perceived usefulness.

Supported

H4: A customer's confirmation of an e-commercial website positively affects a customer's online shopping satisfaction.

Supported

H5: A customer's perceived usefulness of an e-commercial website positively affects a customer's online shopping satisfaction.

Supported

H6: A customer's shopping satisfaction at an e-commercial website positively affects a customer's online repurchase intention.

Supported

H7: A customer's perceived ease of use of an e-commercial website positively affects a customer's trust.

Supported

H8: A customer's trust in an e-commercial website positively affects a customer's perceived usefulness.

Supported

H9: A customer's trust in an e-commercial website positively affects a customer's online

Not sup-

repurchase intention.

ported

H10: A customer's high enjoyment of shopping at an e-commercial website positively affects a customer's online repurchase intention.

Supported

H11: A customer's perceived usefulness affects a customer's online repurchase intention more strongly than perceived enjoyment.

Supported

There is a positive relationship between perceived ease of use and perceived usefulness, between confirmation and perceived usefulness, between confirmation and satisfaction and between perceived usefulness and satisfaction. The authors have also tested hypotheses concerning the relationships between perceived usefulness and trust, between trust and perceived usefulness, between perceived usefulness and online repurchase intention, between satisfaction and online repurchase intention and between perceived enjoyment and online repurchase intention. The only hypothesis which was not validated was that of the relationship between trust and online repurchase intention. Previous internet shopping experience is not included in this model although it might have a direct impact on customers' intention to repurchase online. This latter point was explained in Chen's model. In the Table 2 we provide summary of the hypotheses tested in the Wen, Prybutok, Xu's model [Wen, Prybutok, Xu, 2011].

Chen's Model. Similarly to [Wen, Prybutok, Xu, 2011], Y.-Y. Chen revisited the TAM and ECM models [Chen, 2012]. Chen extended the theoretical framework by considering social cognitive theory and social learning theory [Chen, 2012]. The social congnitive theory posits that learning occurs through the reciprocal interaction of the person, environment and behavior. The theory also states that self-confidence, controllability, motivation to succeed have an impact on individuals' cognition [Bandura, 1986]. The social learning theory posits that learning is a cognitive process that must be considered in its social context because individuals learn through observation of others.

Learning has a mimetic dimension [Ormrod, 1999]. In the Table 3, we are providing the summaries of the hypothesizes tested in the Y.-Y. Chen's model [Chen, 2012].

Table 3

Hypothesis tested in the Chen's model

Hypothesis

Result

H1a: Positive Internet shopping experience has a positive impact on Internet shopping

Supported

self-efficacy.

H1b: Positive Internet shopping experience has an Internet shopping experience has a

Supported

positive impact on perceived usefulness.

H1c: Positive Internet shopping experience has a positive impact on confirmation.

Supported

H1d: Positive Internet shopping experience has a positive impact on satisfaction.

Supported

H2a: Behavior modeling has a positive impact on Internet shopping self-efficacy.

Not supported

H2b: Behavior Modeling has a positive impact on perceived usefulness.

Supported

H2c: Behavior Modeling has a positive impact on confirmation.

Supported

H3a: Internet shopping self-efficacy has a positive impact on perceived usefulness.

Supported

H3b: Internet shopping self-efficacy has a positive impact on confirmation.

Not supported

H3c: Internet shopping self-efficacy has a positive impact on repurchase intention.

Supported

H4a: Confirmation has a positive impact on satisfaction.

Supported

H4b: Confirmation has a positive impact on perceived usefulness.

Supported

H4c: Confirmation has a positive impact on perceived ease of use.

Supported

H5a: Perceived usefulness has a positive impact on satisfaction.

Supported

H5b: Perceived usefulness has a positive impact on repurchase intention.

Supported

H6a: Perceived ease of use has a positive impact on perceived usefulness.

Supported

H6b: Perceived ease of use has a positive impact on satisfaction.

Supported

H6c: Perceived ease of use has a positive impact on repurchase intention.

Supported

H7: Satisfaction has a positive impact on repurchase intention.

Supported

The above discussed four models are summarized in the Table 4.

Table 4

Comparison of the frameworks

TAM

[Davis, 1989]

ecm

[Bhattacherjee, 2001]

wen, Prybutok, Xu's model [Wen, Prybutok, Xu, 2011]

Chen's model [Chen, 2012]

1

2

3

4

5

Aim

Consumer's behavioral intention

Post-adoption intention

Online repurchase intention

Online repurchase intention

Research

question

What causes people to accept or reject information technology?

What are the salient motivations underlying IS users' intention to continue using an IS after its initial acceptance?

How do these motivations influence continuance intention?

How to explain customer continued use of an e-commerce website in an integrated model?

Why do consumers go Internet shopping again?

Understanding the antecedents of repurchase intention

Research

design

Quantitative

Quantitative

Quantitative

Quantitative

Data

2 studies:

1) Field study with a sample of 120 users in Canada;

2) Lab study with 40 participants from MBA Students in Boston

Cross-sectional field survey of online banking users; 1 000 online customers selected from a customer base of 1 mln users

Paper-based questionnaire; 230 college students in US

Questionnaire; 463 respondents, who have already had an online shopping experience from the online virtual store of 7-Eleven in Taiwan

Findings

Relationship between perceived ease of use and perceived usefulness

Importance of satisfaction with IS use having an impact on users' continuance intention.

Perceived usefulness predicting acceptance intention

Consumers' intention to repurchase online.

Impact of marketing and social psychology.

Confirmation of TAM model adapted to IT continuance usage

Continuance models.

Consumers repurchase intention.

Consistent with existing TAM and ECM

2. Hypotheses development

Studied by [Radner, Rothschild, 1975; Kar- hanna, Straub, Chervany, 1999], perceived ease of use is defined as “the extent to which a consumer perceives the ease of interaction with the e-commerce website and is able to receive the product information that he or she needs” [Wen, Prybutok, Xu, 2011, p. 15]. An easy-to-use website will encourage consumers to revisit it. A website is considered user-friendly when users can easily find the information they need, when it is easy to navigate and is visually attractive [Rice, 1997].

Inversely, if customers have difficulties finding and ordering a product on a website compared to competitors' websites, the website is considered to be difficult to use [Chen, 2012].

According to [Chen, 2012], prior Internet shopping experience increases the probability of users' expectations to be confirmed and satisfied. And past experience also generates future expectations. H1. Positive Internet shopping experience has a positive impact on the perceived ease of use of household equipment websites.

Confirmation was defined by A. Bhat- tacherjee as a cognitive belief representing the extent to which consumers' ex-ante expectations of service use were met in reality and refers to this evaluation process [Bhattacherjee, 2001]. Prior online shopping experience has a strong impact on the users' future online shopping behavior [Chen, 2012]. A positive perceived experience has a positive effect on users confirmation, because it is a factor in satisfying current expectations and in creating future ones [Chen, 2012]. Users whose initial expectations were met are more likely to expect positive perceived usefulness and confirmation in future. H2. Positive Internet shopping experience has a positive effect on confirmation in household equipment online stores. Perceived usefulness was defined as “the degree to which a person believes that using a particular system would enhance his or her job performance” [Davis, 1989, p. 320].

Other similar definitions were given by [Ka- rahanna, Straub, Chervany, 1999]. In the TAM model presented by Davis and in [Chau, 2001], it is argued that perceived usefulness has a direct and significant effect on behavior intentions. Chen finds that past internet shopping experiences has an impact on perceived usefulness [Chen, 2012].

A website is perceived as useful if it is useful, effective, helpful, functional, necessary and practical [Zumpe, Van der Heijden, 2007].

H3. Positive Internet shopping experience is positively related to perceived usefulness in household equipment online shopping. The first definition of satisfaction was given by [Locke, 1976, p. 1300] as “a pleasurable or positive emotional state resulting from the appraisal of one's job”.

The concept was then understood more broadly as “the summary psychological state resulting when the emotion surrounding disconfirmed expectations is coupled with the consumer's prior feelings about the consumption experience” [Oliver, 1981, p. 29].

Woodruff, Cadotte and Jenkins argue that if a consumer's perception of a product or a service is positive, his/her satisfaction in the product and in the overall service will be higher [Woodruff, Cadotte, Jenkins, 1983]. Shankar, Smith and Rangaswamy pointed out that users with the highest level of satisfaction are those that have had satisfying online shopping experience [Shankar, Smith, Rangaswamy, 2003].

When they have positive experiences in online shopping, individuals become more proficient and less intimidated by the process, which considerably improves their satisfaction [Chen, 2012]. H4. Positive Internet shopping experiences have a positive effect on satisfaction in household equipment websites. D. Kim, D. Ferrin and H. Rao understand trust as “a multidimensional concept related to various targets: sales person, product, company and Internet trust”. They define online consumer trust as “the consumer's subjective belief that the selling party or entity on the Internet will fulfill its transactional obligations as the consumer understands them” [Kim, Ferrin, Rao, 2008, p. 554].

Definitions of trust have been given by various scholars (e. g. [Gefen, Karahanna, Straub, 2003; Kim, Ferrin, Rao, 2008; Zhu, Lee, O'Neal, 2011]). They argue that trust is more important in the e-commerce environment than in traditional commerce because of the distance and other personal factors [Wen, Prybutok, Xu, 2011].

Privacy and security practices, website design -- particularly navigation and presentation -- brand strength and order fulfill - ment are all antecedents of online trust because consumers wish to protect the confidentiality of their data and want their expectations to be met [Bart et al., 2005].

Consumers have more trust in websites they have previously used for shopping [Bart et al., 2005] and when an online vendor is available [Wen, Prybutok, Xu, 2011]. D. Gefen and D. Straub integrated the construct of trust within the TAM framework and explained that trust influenced usage intention directly and indirectly via the perceived ease of use [Gefen, Straub, 2003].

H5. Positive perceived ease of use positively influences trust in household equipment websites. As already mentioned, the perceived ease of use of a website encourages users to revisit it [Rice, 1997]. The online shopping is not only means of acquiring goods or services but it can also provide enjoyment [Kim, Ammeter, 2008]. If a website brings enjoyment to consumers through positive perceived ease of use, they will be more likely to return to that website and to make other purchases.

H6. Positive perceived ease of use is positively related to perceived enjoyment in household equipment websites. According to TAM, perceived ease of use and perceived usefulness are considered as predictors of behavioral intentions [Davis, 1989]. Perceived ease of use is an antecedent of perceived usefulness [Davis, 1989; Gefen, Karahanna, Straub, 2003]. If users interact easily with a website, can easily find product information or pay online, they will consider their online shopping more useful [Wen, Prybutok, Xu, 2011].

H7. Positive perceived ease of use is positively related to perceived usefulness of household equipment websites. In the ECM, A. Bhattacherjee pointed out that in the post-acceptance stage, the perceived usefulness was influenced by users' confirmation level [Bhattacherjee, 2001].

The author explained that post-purchase expectation can be enhanced if consumers see benefits in the product or service that they had not expected initially. On the contrary, perceived usefulness can decrease if users' expectations (confirmation) are not met. This argument has been presented by several authors [Wen, Prybutok, Xu, 2011; Bhattache- rjeein, 2001; Liao, Palvia, Chen, 2009; Hong, Thong, Tam, 2006]. Chen explained that if consumers' expectations of their online experience are confirmed, their perception of the website will be enhanced [Chen, 2012]. H8. Confirmation of expectations is positively related to perceived usefulness of household equipment websites.

A. Bhattacherjee notes that customers' pre-purchase expectation and a positive perceived performance of the product once they have received and used it will confirm their expectations of the product (confirmation) and will increase their satisfaction [Bhattacherjee, 2001].

Expectation and perceived performance are two antecedents of confirmation [Chen, 2012]. If customers are satisfied and if the product or service meet their expectations, they will be satisfied. H. Kuan, G. Bock and V. Vatthanophas explained that perceived quality information, such as the website's content, accuracy, and format, positively influence users' satisfaction [Kuan, Bock, Vatthanophas, 2008]. If the performance of the product meets the customers' expectations, the latter will be confirmed and the customers' satisfaction will increase, which will influence them to visit the website again and to purchase products from it again. Bhattacherjee also argues that confirmation is an antecedent of customers' satisfaction [Bhattacherjee, 2001]. H9. Confirmation of expectations is positively related to satisfaction in household equipment websites. Trust was defined as a predictor of perceived usefulness of a website because it helps enhance the users' shopping comfort [Zhu, Lee, O'Neal, 2011]. When e-vendors improve consumers' trust by meeting their expectations, consumers will then perceive the website as useful. According to [Gefen, Karahanna, Straub, 2003], trust positively influences perceived usefulness of a website.

H10. Trust in a household equipment website is positively related to its perceived usefulness. Intention of continued purchase was defined as “the likelihood that an online customer will return to the website and purchase again in the future” [Davis, 1989]. As mentioned previously, trust in e-commerce is extremely important and influences consumers' intention to purchase online [Wen, Prybutok, Xu, 2011]. Gefen, Karahanna and Straub have argued that online customers are influenced by their trust in the e-vendor [Gefen, Karahanna, Straub, 2003]. Zhu, Lee and O'Neal find that trust in an online vendor has a significant impact on purchase intention by reducing the user's perceived risk of online shopping [Zhu, Lee, O'Neal, 2011]. Wen, Prybutok, Xu's study did not confirm the relationship between trust and online repurchase intention [Wen, Prybutok, Xu, 2011]. Validation of the hypothesis requires further empirical studies in other industry contexts.

H11. Trust is positively related to intention of continued purchase from an online household equipment store. As mentioned above the construct “perceived enjoyment” refers to the extent to which using an online store is enjoyable.

Online shopping enjoyment, just as in-store shopping enjoyment, is important as it influences consumers' intention and behavior [Wen, Prybutok, Xu, 2011]. In the prepurchase phase, customers are curious and concerned about the ease of use of the website and privacy and security issues. Perceived ease of use and trust are the most important factors in their purchase decision. In the purchase phase, if customers enjoy their visit to the website, they are more likely to make the decision to purchase an item and to visit and purchase products again. In this respect, Wen, Prybutok and Xu argued that shopping enjoyment has a positive impact on users' intention to return [Wen, Prybutok, Xu, 2011].

H12. Perceived enjoyment is positively related to users' intention of repurchasing household equipment online. When websites are perceived as useful, clients are more likely to be satisfied, and therefore to visit the online store again and make new purchases [Bhattacherjee, 2001; Hong, Thong, Tam, 2006; Liao, Palvia, Chen, 2009; Wen, Prybutok, Xu, 2011]. Several articles [Devaraj, Fan, Kohli, 2002; Chen, 2012] argued that perceived usefulness had an impact on satisfaction in the e-commerce context.

H13. Perceived usefulness is positively related to satisfaction in household equipment websites. We make a clear distinction between online purchase intention and online repurchase intention.

The first refers to the initial purchase intention, which is related to customer conversion. The concept of customer conversion refers to the process through which a prospective online customer is converted into a buying customer [Kuan, Bock, Vatthanophas, 2008].

The second is the intention of continued purchase, which is also associated with customer retention. This refers to how an online store ensures that its existing customers remain its customers and continue purchasing from the store [Kuan, Bock, Vatthanophas, 2008].

Bhattacherjee's ECM [Bhattacherjee, 2001], as well as Chen's [Chen, 2012] and Wen, Prybutok, Xu's [Wen, Prybutok, Xu, 2011] models validate the hypothesis of a relation between perceived usefulness and online repurchase intention.

H14. Perceived usefulness is positively related to intention of repurchasing household equipment online. The relationship between satisfaction and continued intention was examined in Bhattacherjee's ECM [Bhattacherjee, 2001]. Wen, Prybutok and Xu define satisfaction as “the summary psychological state resulting when the emotion surrounding discon- firmed expectations is coupled with the consumer's prior feelings about the consumption experience” [Wen, Prybutok, Xu, 2011, p. 16]. Similarly to [Bhattacherjee, 2001; Hong, Thong, Tam, 2006; Liao, Palvia, Chen, 2009; Wen, Prybutok, Xu, 2011], we anticipate that there is a positive relationship between satisfaction and information systems continuance usage intention.

H15. Satisfaction is positively related to users intention to purchase household equipment online again. Based on classic theories and models such as the TAM [Davis, 1989], the information systems continuance and ECM [Oliver, 1980; Bhattacherjee, 2001] and more recent models developed by Chao Wen and co-authors [Wen, Prybutok, Xu, 2011] and Chen [Chen, 2012], we have formulated fifteen hypotheses summarized in Table 5. We have also developed the corresponding conceptual framework that incorporates all discussed constructs that influence online repurchase intention in the household equipment market (Fig. 1).

Table 5

Hypothesizes and the support from the literature

Hypothesis

Implied relationship

Support by previous research

1

2

3

H1

PE ^ PEOU

Never studied before

H2

PE ^ CON

[Chen, 2012]

H3

PE ^ PU

[Chen, 2012]

H4

PE ^ SAT

[Woodruff, Cadotte, Jenkins, 1983].

[Shankar, Smith, Rangaswamy, 2003].

[Chen, 2012]

H5

PEOU ^ TRU

[Gefen, Karahanna, Straub, 2003].

[Wen, Prybutok, Xu, 2011]

H6

PEOU ^ ENJ

Never studied before

H7

PEOU ^ PU

[Davis, Bagaozzi, Warshaw, 1989].

[Gefen, Karahanna, Straub, 2003].

[Wen, Prybutok, Xu, 2011]. [Chen, 2012]

H8

CON ^ PU

[Bhattacherjee, 2001]. [Wen, Prybutok, Xu, 2011]. [Chen, 2012]. [Liao, Palvia, Chen, 2009].

[Hong, Thong, Tam, 2006]

H9

CON ^ SAT

[Bhattacherjee, 2001]. [Hong, Thong, Tam, 2006]. [Wen, Prybutok, Xu, 2011]. [Chen, 2012]

H10

TRU ^ PU

[Wen, Prybutok, Xu, 2011].

[Zhu, Lee, O'Neal, 2011]

H11

TRU ^INT

[Wen, Prybutok, Xu, 2011]

H12

ENJ ^ INT

[Chen, 2012]

H13

PU ^ SAT

[Bhattacherjee, 2001]. [Wen, Prybutok, Xu, 2011]. [Chen, 2012]

H14

PU ^ INT

[Bhattacherjee, 2001]. [Wen, Prybutok, Xu, 2011]. [Chen, 2012]

H15

SAT ^ INT

[Bhattacherjee, 2001]. [Hong, Thong, Tam, 2006]. [Wen, Prybutok, Xu, 2011]. [Chen, 2012]

Notes: PEOU -- perceived ease of use; CON -- confirmation; TRU -- trust; PU -- perceived usefulness; SAT -- satisfaction; ENJ -- perceived enjoyment; PE -- past internet shopping experiences; INT -- online repurchase intention.

3. Methodology

In order to simultaneously estimate several relationships, structural equation modeling is an appropriate technique for our analysis. It was previously used in other studies on the same subject (e. g. [Wen, Prybutok, Xu, 2011; Kim et al., 2012]).

Individual Constructs. We used the following reflective constructs: perceived ease of use (PEOU) [Davis, 1989; Gefen, Karahanna, Straub, 2003; Bignй-Al- caniz et al., 2008], confirmation (CON) [Hong, Thong, Tam, 2006], trust (TRU) [Gefen, Karahanna, Straub, 2003; Hassanein, Head, 2007], perceived usefulness (PU) [Davis, 1989; Bignй-Alcaniz et al., 2008], satisfaction (SAT) [Hong, Thong, Tam, 2006], perceived enjoyment (ENJ) [Van der Heijden, 2004; Hassanein, Head, 2007], past internet shopping experiences (PE) [Zeithaml, Para- suraman, Malhorta, 2002; Doll et al., 2004], online repurchase intention (INT) [Bhatta- cherjee, 2001; Devaraj, Fan, Kohli, 2002]. This study has used a perceptual measurement method based on the point of view of e-customers. In subjective performance indicators are highly correlated with objective performance indicators [Dess, Robinson, 1984]. We have chosen individuals' perception (i. e. opinions and feelings) as the unit of analysis. The data was collected from e-customers. Consequently, our study takes the individual as its unit of analysis. The measurement scales used in our empirical studies are detailed in Table 6.

Table 6

Measurement scales of the constructs

construct

Item

1

2

Perceived ease of use (PEOU) [Davis, 1989; Gefen, Karahanna, Straub, 2003; Bignй-Alcaniz et al., 2008]

The online shopping website is easy to use.

The online shopping website is flexible to interact with.

It is easier to use the Internet to find products that I want to buy

Confirmation (CON)

[Hong, Thong, Tam, 2006]

The last experience with shopping online was better that I expected.

The service level provided by the website was better than what I expected. Most of my expectations from shopping online were confirmed

Trust (TRU)

[Gefen, Karahanna, Straub, 2003; Hassanein, Head,

2007]

I feel safe in my transactions with the website.

I believe the website can protect my privacy.

I select online stores, which I believe are honest.

I feel that this online vendor would provide me with good service.

I feel that the online vendor is trustworthy

Perceived usefulness (PU) [Davis, 1989; Bignй-Alcaniz et al., 2008]

Using the Internet enables to finish the shopping tasks more quickly.

Using the Internet for shopping help to make better purchase decisions. Using the Internet make it easier to make purchases.

Using the Internet for shopping save money.

Find using the Internet for shopping useful

Satisfaction (SAT) [Hong, Thong, Tam, 2006]

I was very satisfied with my overall online shopping experience.

I was very pleased with my overall online shopping experience.

I was very contented with my overall shopping experience.

I was absolutely delighted with my overall shopping experience

Perceived enjoyment (ENJ) [Van der Heijden, 2004; Hassanein, Head, 2007]

I found my visit to this website interesting.

I found my visit to this website entertaining.

I found my visit to this website enjoyable.

I found my visit to this website pleasant

Past Internet shopping experiences (PE) [Zeithaml, Parasuraman, Malhorta,

2002; Doll et al., 2004; Khalifa, Liu, 2007]

I am relatively satisfied with the Internet shopping experience I have had on Internet shopping site.

Compared to other Internet shopping sites, I am satisfied with the shopping experience I have had on this Internet shopping site.

I am relatively satisfied with the product information provided on

Internet shopping site.

I am relatively satisfied with the additional services (order/delivery/ return) provided on Internet shopping site

Intention to repurchase nline (INT) [Bhattacherjee, 2001; Devaraj, Fan, Kohli, 2002]

I intend to continue using online shopping rather than discontinue its use. My intention is to continue using online shopping rather than use traditional shopping.

If I could, I would like to continue shopping online as much as possible

Data Collection. An electronic survey platform was used for data collection, which covered the period between August and October 2014. To conduct the questionnaire, the non-probability sampling was used. In this way, we have asked questions to understand what experiences they have already had and if they were aware to re purchase other products, specifically household products. A questionnaire has been developed initially in English and then translated into French. The targeted population consisted of all people who had bought household equipment at least once through the IKEA website. We have been targeting these specific customers because IKEA is the leader of the household equipment market. IKEA has been maintaining this leadership for the past 9 years. IKEA represents 19.4% of the market share in France.

Two pre-tests were performed prior to data collection to ensure the reliability and validity of the questionnaire. The first pre-test consisted of listening to feedback from 5 potential respondents so as to ensure that the questions were clear and well understood, and where it is necessary to reformulate the questions on the basis of the feedback.

The second pre-test was administered to 30 respondents and the data analyzed via SPSS to test the validity and reliability of the questionnaire. In our pre-test sample, we checked the reliability of the constructs.

The Cronbach's alpha test on all constructs showed a reliability of over 0.7 (PU: 0.705; PEOU: 0.714; TRU: 0.789; ENJ: 0.857; PE: 0.814; SAT: 0.880; CON: 0.805; INT: 0.756).

We could, therefore, proceed with the data collection. In total, 234 respondents answered the questionnaire in final data collection. We then used list-wise deletion, which resulted in a final sample of 218 answers. A test was conducted to compare early and late respondents and to assess the non-response bias [Armstrong, Overton, 1977]. The average values for the first 10 respondents were compared with those of the last 10 respondents. There was no statistical significance difference between the two groups in each case (p > 0.05). Figure 2 contains information regarding demographic characteristics (gender, occupation, income, age) of our total sample.

Fig.2. Demographic characteristics of the sample

4. Analysis

Validity. Prior to the analysis, we tested the validity of our data. Several academic experts established that the measurement scales were appropriate for measuring the constructs. Furthermore, all measurement scales are widely used in the literature [Babbie, 2001] and more specifically in academic journals in marketing (MIS Quarterly, Information System Research, Journal of Management Information Systems, Online Information Review, E-service Journal, Human-computer Studies and Journal of the Academy of Marketing Science). The Cronbach's alpha reliability tests [Rust, Cooil, 1994; Zwick, 1988] showed a satisfactory reliability with a coefficient of above 0.70 for all factors (PU: 0.784; PEOU: 0.764; TRU: 0.816; ENJ: 0.887; PE: 0.815; SAT: 0.837; CON: 0.810; INT: 0.772). The results indicate acceptable consistency of the measurement items [Nunnally, 1978]. To assess the convergent validity, we checked that the correlation between items of the same construct was at least 0.3 (PU: 0.525; PEOU: 0.424; TRU: 0.545; ENJ: 0.661; PE: 0.522; SAT: 0.604; CON: 0.479; INT: 0.385). We also checked that the factor loadings were significant and greater than 0.7. To assess the discriminant validity, we looked at correlation coefficients between the measures of a construct and measures of conceptually different constructs. The low to moderate correlations between the items of the constructs indicate discriminant validity. Finally, to assess the nomological validity, we analyzed two by two correlations within constructs. Since all the signs of the correlations make sense, nomological validity is confirmed.

Normality and Multicollinearity. To assess the normality of our data, we checked that all factors had loadings greater than 0.5. The skew index is below 3 and the kurtosis index is below 10, suggesting that the data is normally distributed. We assessed multicollinearity by calculating bivariate correlations and variance inflation factors. Bivariate correlations did not exceed 0.85 (PU: 0.579; PEOU: 0.677; TRU: 0.649; ENJ: 0.823; PE: 0.560; SAT: 0.686; CON: 0.682; INT: 0.678). Thus, we find no multicollinearity in our dataset.

Confirmatory Factor Analysis. We have been running a confirmation factor analysis (CFA) using AMOS. CFA of measurement model showed a good fit (CMIN/ df = 1.920, CFI = 0.917, IFI = 0.918, NFI = = 0.844, RMSEA = 0.066). The results of the measurement model were favorable in that all items loaded significantly on their appropriate factor. All standardized factor loadings were over 0.5 and highly significant (p < 0.001), which indicates good convergent validity among the instruments of each construct [Bagozzi, Yi, 1988].

CFA has been performed to assess reliability, convergent validity and discriminant validity of the measurement model. Cronbach's alpha and composite reliability facilitate the assessment of the construct reliability. Composite Reliabilities (CR) of all constructs are greater than the 0.60 threshold [Bagozzi, Yi, 1988] which ensure the reliability of our constructs. In our data, the CR goes from 0.783 (PEOU) to 0.892 (ENJ) which ensure a good reliability (Table 7).

Table 7

Composite reliability and average variance extracted

variable

CR

AVE

INT

0.792

0.568

PEOU

0.783

0.552

CON

0.807

0.583

TRU

0.822

0.607

PU

0.785

0.549

SAT

0.839

0.637

ENJ

0.892

0.735

In our study, the standardized loadings are above 0.5 and significant. The results of the measurement model were favorable in that all items loaded significantly on their appropriate factor.

C. Fornell and D. Larcker argued [Fornell, Larcker, 1981] that if the Average Variance Extracted (AVE ) is greater than 0.5 of the total variance, convergent validity is established. All items have standardized loadings greater than 0.5 and AVE greater than 0.5, providing evidence of the measures' convergent validity (Table 7).

To assess the discriminant validity, the Fornell-Larcker criterion compares the square root of the constructs' AVEs with the construct correlations [Fornell, Larcker, 1981]. This analysis shows that across all model setups and constructs, each latent variable shares more variance with its own block of indicators than with another latent variable representing a different block of indicators.

Measurement model showed acceptable discriminant validity for all constructs except PEOU, CON, SAT and PE. We decided to maintain the behavioral and emotional dimensions as separate constructs in the measurement model.

Structural Equation Modeling. SPSS and AMOS have been used to perform the statistical analysis of the data.

First, we conducted model specification tests. In our model, which we presented in the literature review, there is one exogenous multi-item latent construct (PE). There are seven multi-item endogenous latent constructs (PEOU, PU, TRU, CON, SAT, PE, ENJ). There are 72 regression weights, 40 of which are fixed and 32 of which are estimated. We have 0 covariance and 33 variances, all of which are estimated. In total, there are 105 parameters, 65 of which are to be estimated.

Second, we then performed the model identification. Our model is over-identified: there are 325 distinct sample moments and 65 parameters to be estimated, thereby leaving 260 degrees of freedom. The chi-square value is 492,880 (p < 0.001).

Third, we then estimate the model. Maximum likelihood estimation can be used since the model is over-identified (df = 260), the observed variables are normally distributed, the latent variables are normally distributed, and we have more than 90 observations.

Fourth, the model evaluation is performed to assess the validity of our measurement model. The overall model fit was good (CMIN/df = 1.896, CFI = 0.915, IFI = = 0.916, RMSEA = 0.065). The CMIN/df ratio below 3 indicates a favorable model fit [Marsh, Hocevar, 1985]. Consequently, our CMIN/df is 1.896, which indicates a good fit.

The use of multiple fit indices is recommended [Bagozzi, Yi, 1988]. It is important to consider using incremental fit indices such as CFI and IFI. CFI and IFI of over 0.9 indicate a good model fit [Arbuckle, Wothke, 1995] which is the case in our analysis. Our CFI (0.915) and our IFI (0.916) indicate a good incremental fit. For a good fit, RMSEA of our complex model and for our sample size should be below 0.10. Our RMSEA is 0.065, indicating an acceptable model fit. All standardized factor loadings were over 0.5 and highly significant (p < 0.001), which indicates good convergent validity among the instruments of each construct [Bagozzi, Yi, 1988]. The modification indices in the confirmatory factor analysis for omitted paths showed no significant crossloading among the items (i. e. all results are below 0.85). It indicates good discriminant validity [Kline, 1998]. All those results support the overall validity of the constructs measured in this study.

Results. Table 8 presents the estimates and path significance.

Table 8

Results

Path

Estimate

p-value

Hypotheses support

H1

PE ^ PEOU

0.617

0.000

Strong support

H2

PE ^ CON

0.736

0.000

Strong support

H3

PE ^ PU

0.172

0.405

No support

H4

PE ^ SAT

0.641

0.000

Strong support

H5

PEOU ^ TRU

0.943

0.000

Strong support

H6

PEOU ^ ENJ

0.818

0.000

Strong support

H7

PEOU ^ PU

0.503

0.030

Support

H8

CON ^ PU

0.043

0.761

No support

H9

CON ^ SAT

0.389

0.000

Strong support

H10

TRU ^ PU

0.053

0.581

No support

H11

TRU ^INT

-0.052

0.375

No support

H12

ENJ ^INT

-0.112

0.009

Supported (negative relationship)

H13

PU ^ SAT

0.067

0.324

No support

H14

PU ^ INT

0.404

0.000

Strong support

H15

SAT ^INT

0.206

0.008

Support

Our data support hypotheses 1, 2, and 4 but does not support hypothesis 3. Past internet shopping experience has a highly significant and positive effect on perceived ease of use (p < 0.001; estimate: 0.617; H1). Past internet shopping experience has a very significant and very strong impact on confirmation (p < 0.001; estimate: 0.736; H2) and on online satisfaction (p < 0.001; estimate: 0.641; H4). However, there is no correlation between prior experience and customers' perception of the website (H3). The online buyers' perception of a website's ease of use has a significant and positive impact on trust, perceived enjoyment, and perceived usefulness. Our data support hypotheses 5, 6, and 7. The perceived ease of use of a household equipment retail website has a very significant and extremely strong impact on trust (p < 0.001; estimate: 0.943; H5). The perceived ease of use of household equipment website has a very significant and very strong impact on perceived enjoyment (p < 0.001; estimate: 0.818; H6). The perceived ease of use of household equipment website has a significant and strong impact on perceived usefulness (p < < 0.05; estimate: 0.503; H7).

Expectation-confirmation from users of household equipment retail websites does not influence their perceived usefulness. However, the latter has a significant effect on satisfaction. Our data does not support hypothesis 8 but supports hypothesis 9. Confirmation of expectations on a product does not have a significant impact on the website's perceived usefulness (H8). However, confirmation has a very significant and positive impact on users' satisfaction with the website (p < 0.001; estimate: 0.389; H9). When examining the antecedents of customers' intention to continue purchasing household equipment online, we identified two significant and positive predictors: perceived usefulness and satisfaction; one nonsignificant predictor: trust; and one significant and negative predictor: perceived enjoyment. While H14 and H15 are supported by the analysis, H11 is not and we obtain counterintuitive results concerning H12.

Trust is not a predictor of the website's perceived usefulness (H10). Trust is not a predictor of online repurchase intention either (H11). The perceived usefulness of the website is not a predictor of satisfaction (H13). However, perceived usefulness has a very significant and positive impact on online repurchase intention (p < 0.001; estimate: 0.404; H14). The website satisfaction has a significant and positive impact on online repurchase intention (p < 0.01; estimate: 0.206; H15). The perceived enjoyment has a significant and negative impact on the intention to repurchase online (p < 0.01; estimate: -0.112; H12).

5. Discussion

Consumers' Past Experience of online Shopping. For online buyers, past online shopping experience has strongly significant and positive impact on perceived ease of use, confirmation, and satisfaction, but has no significant effect on perceived usefulness.

Online customers acquire important skills and experience in online buying and will as a result consider online purchasing an easy experience. In other words, familiarity with the website enables the online clients to navigate and find the information they need more easily. In line with Chen's findings [Chen, 2012] we argue that past internet shopping experience has a significant and strong impact on confirmation and on online satisfaction. Past online shopping experience has a significant and positive impact on users' subsequent experiences, in terms of beliefs and attitude. Future expectations are created based on past internet shopping experience in the household market. Past internet shopping experience has a strong and positive impact on the online purchasing satisfaction. It means that experienced online clients feel more positive about using the website than those who have no online shopping experience. However, the fact that customers have prior online shopping experiences does not necessarily mean that they will perceive the website to be useful, effective, helpful, functional, or practical more than a person with less experience.


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