The Determinants of Customer
Satisfaction in Online and Offline
Medium
The Determinants of Customer Satisfaction
in Online and Offline Medium
Master Thesis
University of Groningen
Faculty of Economics and Business
Department of Marketing
Name:
Raja Sharah Fatricia
Student number:
1944231
Address:
Planetenlaan 495
9742 HS Groningen, The Netherlands
E-mail:
eau_de_cherie@yahoo.com
Telephone:
+31 (0)6 336 41 798
First Supervisor:
Dr. Jia Liu
Management summary
This research explores the factors that influence customer satisfaction in online and offline environment. The main objective of this research is to discover in-depth knowledge about determinants of customer satisfaction both in online and offline environments. In this research, the satisfaction determinants are categorized into two factors which are common factors and specific factors. Common factors are the factors that are expected to have an effect in both environments. In contrast, specific factors are the factors that are predicted to have an effect either in online or offline medium. In the following research attempted to answer the main research question: “What are the significant determinants of customer satisfaction in online and offline medium for service area?”
This research builds a conceptual framework where customer satisfaction is being influenced by common factors and specific factors in online and offline medium. To that extend, the use of regression analyses was suitable in order to explore the relationship between the aforementioned factors and customer satisfaction in online and offline environments. Data was collected from respondents across countries namely Indonesia, the Netherlands, Japan, Australia, Germany, France, UK and Malaysia in July 2011, employing a web-based design questionnaire. The final data set consisted of 110 respondents who have experiences in purchasing flight tickets online and offline.
Table of Contents
1. INTRODUCTION ... 5
1.1. Problem Analysis ... 6
1.2. Problem Statements ... 7
1.3. Relevance ... 8
1.4. The Structure of Thesis ... 10
2. THEORETICAL FRAMEWORK ... 11
2.1. General definition of Customer Satisfaction ... 11
2.2. Factors that influence customer satisfaction ... 13
2.3. Online and offline Environments ... 14
2.4. The link between Online/Offline environments and Customer Satisfaction ... 15
2.5. Factors that Influencing Customer Satisfaction in Online and Offline Environments. ... 16
2.6. Conceptual Framework of the Relationship between Online/Offline Services and Customer Satisfaction ... 24
3. RESEARCH METHODOLOGY ... 27 3.1. Research Design ... 27 3.2. Population ... 27 3.3 Sampling Method ... 27 3.4. Procedure ... 27 3.5. Questionnaire Design ... 28 3.6. Statistical Method ... 33 4. RESULTS ... 36 4.1. Respondents Profile ... 36 4.2. Data Descriptions ... 38 4.3. Estimation Results ... 40
5. CONCLUSIONS, RECOMMENDATIONS, LIMITATION AND FURTHER RESEARCH ... 47
5.1. General Conclusions ... 47
5.2. Recommendations ... 48
5.3. Limitation and Further Research ... 50
REFERENCES ... 52
APPENDIX A ... 58
1. Introduction
Faced with increasingly intense competition, falling margins, and customer growing diversification; many companies are seeking to distinguish themselves by providing several shopping channels for customer to consolidate their shopping needs and better serve customers. In this environment, many consumers have become multichannel users (Verhoef, et al, 2007).
Multichannel customer management is the design, deployment, and evaluation of channels to enhance customer value through effective customer acquisition, retention and development (Neslin et al., 2006). Channels typically include the store, the web, catalog, sales- force, third party agency, call center and the like (Neslin and Shankar, 2009). These multichannel environments have several purposes. The multichannel strategy may help firms foster customer loyalty by increasing customer contact points, offering channel selections for the customer‟s convenience and providing diverse type of services (Cassab and Maclachlan, 2006). Moreover, Shankar et al. (2003) argue that the environments may influence customer satisfaction and loyalty differently in the online environment via-a-vis the offline environment. In line with that, the synergy between online and offline environment generated by the seamless integration between the two channels enriches customers‟ experience with the firms, strengthens the brand image of firms, and cultivates customer loyalty in both channels (Bailer, 2006; Harvin 2000). As literature and company budgets continue to show the benefits of these channels environment, more companies are getting onto the bandwagon. This has led to growing demand for companies that offer unique program solutions for consumers, and also the need for documented proof that these programs are successful.
provoke lower customer satisfaction than the offline medium. Moreover, Reicheld and Schefter (2000) argue that purchasing online is considered risky, since customers lack direct contact with the company. Consequently, the customers that use online service have lower satisfaction than those use offline service.
There are a substantial number of empirical studies that examine the characteristics of offline and online medium separately and their relation to the customer satisfaction. For instance, Urban et al. (2000) explain that in online medium that are oriented toward self-service with little human interaction, many conventional service quality dimensions such as the physical appearances of facilities, employees, and equipment, and employees‟ responsiveness and empathy are unobservable. As a result, trust may play a central role in online customer satisfaction. However, the process of trust formation, the interaction of trust with other factors related to the service provider and the environment, and the role of trust in shaping online customer satisfaction. Moreover, online and offline medium differ substantially in terms of how information is accessed and processed, the nature of intermediation, and the process of trading (Barber and Odean 2000, Konana et al., 2000). Degeratu et al. (2001) add that, in the online environment, sensory search information (e.g., visual cues) has a lower impact on choices, price sensitivity is higher due to stronger signaling effects of price promotions, and branding is more valuable only in the absence of factual attribute-related information. However, the study that extensively compare customer satisfaction between online and offline service is rather limited. Therefore, this research will try to fill the gap by comparing between customer satisfaction in online and offline medium. Moreover, this research also tries to find out which factors influence satisfaction in online and offline medium the most.
1.1. Problem Analysis
problems, respect, emotional connection, fair prices and convenience (Berry, 2001). Due to technological breakthroughs and the ease of getting information from the internet, the customers are more knowledgeable about products from different retailers and are very likely to switch between providers, in search of the best offer.
In order to cope with the current business environment, firms need to render their customer loyal. This is hard to obtain, as Frow and Payne (2007) notice that only delighted customers can be considered loyal, while just the satisfied ones are only slightly more loyal than the dissatisfied. Shankar et al (2003) claim that the ease of obtaining information and the frequency of use have a stronger positive effect on overall satisfaction online than offline. Online customers can more easily compare alternatives than offline customers, especially for functional products and services. They add that a competing offer is just a few clicks away on the Internet. Because of these properties of the Web, on the one hand these properties will give advantages to customers. However, many managers fear that the online medium may provoke lower customer satisfaction and loyalty compared to the offline medium, and that increased satisfaction with a service may not lead to higher loyalty when that service is chosen online. Hence, in general, Shankar et al. (2003) could not find that the online medium has a significant effect on overall customer satisfaction. In addition, Reicheld and Schefter (2000) claim that purchasing online is considered uncertain, since customers lack direct contact with the company. Consequently, the customers that use online service have lower satisfaction than those use offline service. Urban et al. (2000) find that trust may play a central role in online customer satisfaction.
1.2. Problem Statements
is presumably the magnitude of the disconfirmation effect that generates satisfaction and dissatisfaction.
In addition, existing studies have focused on customer satisfaction in online and offline discretely. More scholars analyzing about offline environment and relate to customer satisfaction for example Harris et al. (2006); horppu et al. (2008). They demonstrate several factors that affect customer satisfaction in offline medium such as service recovery, brand image. Furthermore, researches also explore the determinants of customer satisfaction in online medium, for instance Ribbink et al. (2004) and Urban et al (2000).
Specifically, Shankar et al. (2003) mention the factors which may influence both customer satisfactions in both online and offline services. Those factors are service provider performance, customers‟ prior experience with the service, the frequency of service use, disconfirmation of time spent choosing service, and ease of obtaining information about the service.
However, since there are no many studies which explore intensively the customer satisfaction both in online and offline service. Hence, this research aims at examining deeply the determinants of customer satisfaction in online and offline services. The main research question is:
“ What are the significant determinants of customer satisfaction in online and offline medium for service area?”
1.3. Relevance
1.3.1. Academic relevance
(1992); Bolton and Lemon (1999); Spreng et al. (1996); Horppu et al. (2008); Harris et al. (2006). They discover that ease of obtaining information, frequency of use, image, service recovery, prior experience are several factors that affect customer satisfaction in offline medium. In addition, researchers not only investigate customer satisfaction in traditional market but also in the online environment separately. Further, another field of research related to online environment has spilled out the academic field, for instance Ribbink et al. (2004); Urban et al (2000); Yoon (2002) concern the effect of satisfaction in placing trust in online channel.
However, the research about factors determining customer satisfaction both in online and offline environment is quite new in the field of academics. There is a key paper that specifically investigates about customer satisfaction both in online and offline channels in service industry (hotel, travel), namely Shankar et al. (2003). They find that the level of customer satisfaction for a service chosen online is the same as when it is chosen offline. They also consider several determinants for customer satisfaction in online and offline medium such as frequency of use, ease of obtaining information, performance, and prior experience. Further, their finding will be applied in this research.
To fill the lack of those studies, this research strives to explore deeply about determinants of customer satisfaction both in online and offline environments. In this research, the satisfaction determinants are classified into two factors which are common factors and specific factors. Common factors are the factors that are expected to have an effect in both environments. Conversely, specific factors are the factors that it is predicted to have an effect either in online or offline medium. Up to now, there is no study examining the determinants of customer satisfaction by classifying common factors and specific factors. It becomes the main contribution and novelty of this research for marketing field.
1.3.2. Managerial Relevance
synergize between online and offline environment. The synergy and integration between the two services in multichannel customer management enriches customers‟ experience with the firms, strengthens the brand image of firms, and cultivates customer loyalty in both channels. The result of this research will be relevant for marketing managers, brand managers, and other managers who have to make important decisions in relating to online and offline environments and the relationship with customer satisfaction.
Another important finding of this research will be beneficial for managers who in charge in online and offline environment. Take for example, a clothing store such as Zara, Mango, Esprit that have both a brick and mortar store as well as online store. Managers can apply the common factors of customers‟ satisfaction such as diversity of products in both channels so that customers will have diversity of products to choose and will be more satisfied. Another example is to make ease of information that offer by firms in order to make customer more efficient in search time and effort. Managers should also spend in advance technologies that seek for the right information and a speedy retrieval. Those are some examples that managers should be taking into account in order to boost customer satisfaction and may lead to customer loyalty.
To sum up, the managerial relevance of this research is particularly for marketers and managers so that they can determine which factors that applicable most to employ in online and offline channel, and put more emphasize in strengthening those factors in order to have a greater of customer satisfaction.
1.4. The Structure of Thesis
2. Theoretical Framework
2.1. General definition of Customer Satisfaction
Conceptually, satisfaction is an outcome of purchase and use resulting from the buyer's comparison of the rewards and costs of the purchase in relation to the anticipated consequences. Operationally, satisfaction is similar to attitude in that it can be assessed as the sum of the satisfactions with the various attributes of the product or service (see Churchill and Surprenant, 1982). Hoyer and MacInnis (2008) state that, customer satisfaction is the feeling that results when consumers make a positive evaluation or feel happy with their decision. Conversely, they claim that, when consumers have a negative evaluation of an outcome, they feel dissatisfaction. Dissatisfaction can be related to feelings of tolerance, distress, sadness, regret, agitation and outrage.
In line with that, satisfaction based on Lovelock and Wirtz (2007), is a person‟s feelings of pleasure or disappointment resulting from a consumption experience when comparing a product‟s perceived performance or outcome in relations to his or her expectations. Hence, satisfaction is defined as an attitude-like judgment following a consumption experience. Most research proves that the confirmation or disconfirmation of pre-consumption expectations is the essential determinant of satisfaction. Lovelock and Wirtz (2007) then formed satisfaction judgments based on this comparison. The resulting judgment is labeled positive disconfirmation if the service is better than expected, negative disconfirmation if it is worse than expected and simple confirmation if it is expected ( see Churchill and Surprenant, 1982; Szymanski and Henard, 2001).
satisfaction ratings to both executive and employee compensation. Nevertheless, providing incentives to maximize customers‟ satisfaction might be detrimental to the firm.
Customer satisfaction is not only for profits, but also can be an important determinant of customer retention which, in turn, has a very strong effect on profitability (Reicheld and Sasser 1990). It thus plays a very prominent role in marketing strategy and public policy formation (Fornell and Wernerfelt 1987). Similar, Kumar and Reinartz (2006) define that in order to retain customers; many firms have focused their attention on increasing customer satisfaction levels. The degree of customer satisfaction is indeed a key measure. However, to what extent customer satisfaction leads to loyalty and profitability is an essential issue to be examined. Traditionally, they agree that customer satisfaction is expected to lead to greater retention or loyalty, which in turn leads to greater profit.
Additionally, Bolton et al (2000) state that marketers have assumed that satisfied customers are more loyal, of which Szymanski and Henard (2001) confirm in their meta-analysis of purchase intentions studies. This adds to the abundance of research that has focused on the positive effects of satisfaction – loyalty relationship. Even so, there is a growing research that shows this relationship to be complex and nonlinear (Oliver 1990, Bolton 1998, Dong 2003). In most cases the satisfaction – loyalty link in dependent on the industry and even on per customer characteristics, of which most positive effects of this link has been with satisfaction consider an important driver of customer loyalty, especially in the retail industry (Dong 2003; Martensen, et al. 2000).
Thereby demonstrating that the customer satisfaction – loyalty link is not always one directional. Such has been substantiated by other researchers that claim the effects of customer loyalty are in reverse, as satisfaction is inconclusive in explaining customer loyalty (Story & Hess 2006; Reichheld 2003). Pleshko & Baqer (2008) further suggests, from this reverse satisfaction – loyalty perspective, it is possible to be a satisfied buyer but not a loyal buyer. In this light, satisfaction can be considered an attitude and thus following the attitude-behaviour definition of loyalty present by Dick & Basu (1994).
2.2. Factors that influence customer satisfaction
A lot of researches have analysed the factors that influence customer satisfaction. In principle, Churchill and Surprenant (1982) mention that there are 3 determinants of customer satisfaction: (1) Expectation, (2) Performance, (3) Disconfirmation. Disconfirmation arises from discrepancies between prior expectations and actual performance. It is presumably the magnitude of the disconfirmation generates satisfaction and dissatisfaction. Confirmed when a product performs as expected; negatively disconfirmed when the product performs more poorly than expected; positively disconfirmed when the product performs better than expected. Dissatisfaction results when a subject's expectations are negatively disconfirmed. In addition, Szymanski and Henard (2001) add the other two factors which is possibly affecting customer satisfaction, which are affect and equity. The satisfaction is not just cognitive but also an affective component. Equity is a fairness, rightness, or deservingness judgment that consumers make in reference to what others receive
According to Johnson and Fornell (1991) product expectations, experience and perceived performance are the factors that affect satisfaction. Past performance information provides a basis for one‟s expectations, attitudes and stored evaluations (Howard, 1989). An individual‟s product experience and resulting access to past performance information should affect satisfaction. They add that, consider a totally new category of products in which customers have no experience and for past performance is not available, expectations will be weak and satisfaction is likely judged relative to the provision of more basic needs (Westbrook and Reilly, 1983). As a customer‟s product experience grows, expectations should become strong and their effect on satisfaction should increase (Johnson and Fornell, 1991).
future) with judgments that implicitly require the use of several possible standards of comparison (ex. Desires, industry norms, equity, best brand).
With regards to desires, desires concretely as the levels of attributes and benefits that a consumer believes will lead to or are associated with higher-level values (Spreng et al, 1996). Only at this concrete level are desires directly comparable to perceived performance. Implicitly or explicitly, people judge the extent to which a product contributes to the attainment of their desired end-states by examining the extent to which the product produces consequences or outcomes or provides attributes or benefits that they believe will be instrumental in leading to the attainment of their higher-level desires (Spreng et al, 1996). For example, a consumer might have as an abstract value the desire to protect his or her family from harm; and this may manifest itself in a desire to buy products that provide the benefit of safety. Thus, they continue, expectations are beliefs about the likelihood that a product is associated with certain attributes, benefits, or outcomes, while desires are evaluations of the extent to which those attributes, benefits, or outcomes lead to the attainment of a person's values. Expectations are future-oriented and relatively flexible, conversely desires are present oriented and relatively stable. Furthermore, based on Ribbink et al (2004) customer satisfaction is closely related to interpersonal trust (Geyskens et al, 1996) and is considered and antecedent of trust (Garbarino and Johnson,1999; Selnes, 1998). They state that customers‟ satisfactory experiences with firms are expected to increase consumers‟ willingness to make more purchase.
To sum up, those are factors that influence satisfaction in general. In the following section this research will be further developed in analyzing online and offline environments and factors that influence customer satisfaction in offline and online environment.
2.3. Online and offline Environments
et al. (1997) claim that a key difference between online and offline shopping is the ability of online consumers to gain more information about both price and non-price attributes. However, according to Degeratu, et al (2000) in both online and offline media, some attribute information relevant for decision making may not be readily available (e.g. search cost for those attributes are high). This takes place because information on an attribute is simply not accessible in a given medium and the information can only be obtained with considerable effort.
The differences between online and offline can be further explained in terms of perceived risk. Purchasing online is considered risky, since customers lack direct contact with the company, i.e. through sales personnel or the physical store (Reicheld and Schefter, 2000), and have to hand over sensitive information, such as credit card numbers, in order to complete the transaction. The absence of interpersonal interaction also suggests that online trust is mainly cognitive, i.e. based on customers‟ judgments of the reliability and capabilities of the merchant or the exchange channel, and not affective trust, i.e. founded on a bond among individuals (McAllister, 1995).
2.4. The link between Online/Offline environments and Customer Satisfaction
2.5. Factors that Influencing Customer Satisfaction in Online and Offline Environments.
The consumer buying process does not end when a customer purchase a product. After making a purchase, the customer consumes the product and then evaluates the experience to decide whether it was satisfactory or unsatisfactory (Hoyer and MacInnis, 2008). Satisfaction is a post-consumption evaluation of how well a store or product meets or exceeds customer expectations. Moreover, this buying process occurs both in an offline and online environments. Based on existing literatures, there are several factors that influence satisfaction in both environments which can be seen in the following table.
Table 1
Determinants of Customer Satisfaction in Online and Offline Services
No. Offline environment References Online Environment References
1. Ease of obtaining information (+)
Oliva et al., 1992 Ease of obtaining information (+)
Degeratu et al., 2000 2. Frequency of use (-) Bolton and lemon,
1999; Vredenburg and Wee(1986)
Frequency of Use (+) Shankar et al., 2003 3. Prior Experience (+) Cadotte et al., 1987;
Vredenburg and Wee (1986)
Prior Experience (+) Cadotte et al., 1987, Shankar et al., 2003 4. Service Recovery (+) Harris et al., 2005 Service recovery (+) Harris et al., 2005 5. Diversity of products (+) Ho and Wu, 1999 Diversity of products (+) Ho and Wu, 1999 6. Technological system (+) Meuter et al., 2000 Technological system(+) Meuter et al., 2000;
Ho and Wu, 1999 7. Attribute performance (+) Spreng et al., 1996;
Shankar et al, 2003
Attribute performance (+) Shankar et al., 2003 8. Payment equity (+) Szymansky and
Henard, 2001
Payment Equity (+) Szymansky and Henard, 2001
9. Disconfirmation of Payment (-)
Bolton and Lemon, 1999
Disconfirmation of Payment (-)
Bolton and Lemon, 1999
10. Brand image (+) Martensen et al., 2000 Brand image (+) Horppu et al., 2008 11. Personal Interactions (+) Alba et al., 1997 Interactivity of website (+) Montoya-Weiss et al.,
2003
12. Trust (+) Yoon, 2002;
Based on table 1.1, we can see that there are common factors that influence satisfaction which can be applied both in online and offline medium (with blue color). However, there are also different factors that can be relevant only for online or offline environment which will be elaborated in the subsequent discussions.
2.5.1. Common Factors affecting Both Online and Offline Environments.
1. Ease of obtaining Information
Ease of obtaining information can be one factor that influences customer satisfaction when determining service provider (Oliva et al., 1992). By relevant information, customers can make better decisions that pilot to higher satisfaction. According to Shankar et al. (2003) easier access to information also tend to augment customer satisfaction with the shopping process (unless there is information overload), which could enhance customer satisfaction with the service as a whole. For example, when customers receive information via brochures or booklets of certain products, it will make customers easier to decide which products they will buy; hence, it will reduce the search time and lead to satisfaction.
Similar to offline, according to Degeratu et al. (2000), online information searches need less effort than offline searches, since they incorporate such advanced features as recommendation systems and decision aid tools. Moreover, Alba et al. (1997) state that consumers benefit from the advantage of screening because they can screen a large number of optional products, which usually outweighs the cost associated with search efforts. Lynch and Ariely (2000) argue that reduced shopping effort and increased screening should increase online satisfaction.
2. Frequency of use
relationship if customers use a service often and the repeated exposure to favorable service encounters may continually lead higher satisfaction. Meanwhile, when the customers use a service often, they start to treat the benefits of the service as a given and expect additional benefits from the service provider overall. The raised expectations may lead to lower expectation. For offline service, prior studies mostly found negative effects than positive effects.
However, the online service could dampen the negative effect of the usage frequency on customer satisfaction. Because the medium service generates expectations that are more consistent with the actual service levels, it mitigates problems associated with frequent users having higher expectations, and therefore, being potentially less satisfied with the service provider. The consistent expectations also lower the mental costs of online choices, improving the customer satisfaction (see Shankar et al., 2003).
3. Prior Experience
A good prior experience with a service provider enhances the likelihood of a constructive evaluation of the current service encounter as well as the overall evaluation of the service provider by affecting their norms and expectations (Shankar et al, 2003). Cadotte et al (1987) discovered that customers‟ past experiences with restaurants affect their evaluations of subsequent dining experiences. Similar, Vredenburg and Wee (1986) find that favorable prior experience resulted in higher satisfaction levels in a study of auto industry.
In line with the offline environment, prior experience can be one of determinants in affecting customer satisfaction in online medium (Cadotte et al., 1987). If a customer has a good experience in using service online, there is a tendency that he or she will be more satisfied and might lead in making repeated purchase. Thus, overall, the more favorable the prior experience the higher the satisfaction. In contrast with frequency of service use, which refers to the quantity of previous use of a service, prior experience refers to the quality of previous experiences with the service provider (Bolton and Drew, 1991).
4. Service Recovery
Oliver, 1980). The service recovery literature shows that this disconfirmation model is appropriate in recovery situations as well (e.g., Boshoff, 1997), because as the positive disconfirmation with a service failure recovery increases, so does satisfaction with the therapy. For example, Boshoff (1999) finds that satisfaction is positively related to the size of the remedy and how quickly the problem is resolved. Furthermore, Spreng et al. (1995) claim that, customer satisfaction with service failure recovery has a greater impact on overall satisfaction than does any other individual aspect of the result of the service delivery. When a service failure occurs, consumers determine whether the failure is caused by themselves or the service provider (Harris et al, 2006). They continue that, consumers tend to blame others or even the situation itself when things go wrong, whereas pleasant outcomes are generally attributed to one‟s self (Folkes, 1988). So, the interval of service recovery plays an important role in satisfaction. The fastest the cure, the consumers will be more satisfied.
It happens as well in the online environment; self service technology acts even more of the service for customers and therefore have more control over the delivery of the service (Rust and Lemon, 2001). Harris et al., (2006) believe that because online customers participate more in the delivery of the service than do offline customers, online customers will be more satisfied with faster service recovery level. The major point is that the recovery level will escort to after purchase intention and satisfaction and it pilots customer to return to the same provider.
5. Diversity of Products
Traditional stores offer variety of products. They have to show the products to customers by displaying the products physically, so that customers can touch them or try them. The variety of products may bring consumers into more choice. Hence, the more diverse the products, the more satisfy consumers because customers can search what they want in the variety of items. For example, a consumer of Zara is looking for a blouse that fits to his/her body, he/she can try the clothes that suit his /her. The diversity of colors or sizes can help the customer to make a decision and satisfy of what he/she purchase.
medium offers more diversification products to the customers. The more the customers get to select, the better the service they feel that they will have. Hence, the customers will be more satisfied (Ho and Wu, 1999).
6. Technological advanced
Meuter et al. (2000) observe about relationship between the Self Service technology (SST) and customer satisfaction. Self-Service Technologies are technological interfaces that enable customers to produce a service independent of direct service employee involvement. Examples of SSTs can be found in ATM machine, Fending Machine, Hotel check out and more. They elaborate three major groups of factors leading to a satisfactory evaluation of an SST experience:
1. Satisfaction is due to the SST‟s ability to bail customers out of immediate or troubling situations. This is possible because of the SST‟s pervasive nature and relatively easy accessibility.
2. SST gives a relative advantage, because the customers believe the SST are more effective at delivering the service than a trained employee.
3. Consumers are still often fascinated that a technology „did its job‟
Comparable to the offline, in online services the technological system includes modern computer network facilities and well structured information system (Ho and Wu, 1999). The online medium utilizes computer systems (e.g., PC or workstation), communication networks, and information systems, which are open to all users all the time (Ho and Wu, 1999). Companies strive to improve the network architecture by increasing and updating to advanced hardware and software in order to better serving their customers and make customers more satisfy with the system.
7. Attribute performance
Likely, in online environment, the actual performance on various services attributes that a customer experiences during a service encounter influences customer satisfaction (Bearden & Teel, 1983; Bolton & Drew, 1991; Mittal et al., 1998; Oliva et al., 1992; Oliver, 1993; Spreng, MacKenzie, & Olshavsky, 1996). Attribute-level performance can affect satisfaction positively (Shankar et al., 2003). Furthermore, Shankar et al. (2003) claim that the better the product quality that customer gain relative to customers‟ expectations, the higher their satisfaction. For example in online environment, since customer cannot touch or try the products, customer can only expect the quality of the product is as expected as it is written on the product information. As it is as expected, the consumer will satisfy and vice versa.
8. Payment Equity
Payment equity is extending well-recognized expectancy-disconfirmation paradigm. In addition, equity itself is defined as a fairness, rightness, or deservingness judgment that consumers make in reference to what others receive (see Szymanski and Henard, 2001). Customer satisfaction also should depend on the perceived fairness of the price/usage trade-off. Specifically, the more equitable a customer believes the price/usage trade-off to be; the more satisfied he or she will be with the service (Bolton and Lemon, 1999). This theory is also applicable in the online environment since in online the perceived fairness of price trade-off is also needed. Hence, the more equitable a customer believes the price/usage trade off to be; the more satisfied he/she will be.
9. Disconfirmation of payment
10. Brand Image
A familiar brand guarantees the consumer a certain level of quality and satisfaction (Horppu et al, 2008). A strong and reliable brand can increase a company‟s products (Norback, 2005), and enhance the company‟s image. Strong brands should eventually result in higher revenue (Aaker, 1991), and convey significant cost savings and brand-extension opportunities (Keller, 2005).
Consumers seem to depend on brands more in the online than in the offline environment (McGovern, 2001), although Harvin (2000) suggests that offline brand power is likely to be transferable to the online environment. Thus, companies with well-known offline brands can benefit from the “halo effect” when trying to establish a new presence on the web (Harvin, 2000). As a consequence, the stronger the image of brands, the customer will be more satisfied.
2.5.2. Different Factors that Only Influence Satisfaction in Online and Offline environment
1. Personal interaction (Only for offline environment)
Personal interaction only can be occurred in a traditional store since in the physical store customers can personally meet face to face with the sales assistant asking for their help or suggestion. According to Alba et al (1997) personal interaction can increase consumers‟ confidence and post purchase satisfaction, due to certain product attributes are more easily observed in person prior to the actual transaction. They claim that interaction store-based retailers have an additional characteristic that radically increases the usefulness of the information available to consumers. Interaction between a customer and sales associate enables store-based retailers to provide information about the attributes that matter to the customer. Therefore, the more personal interaction in offline service will increase customer satisfaction.
2. Interactivity of Websites (Only for the online environment)
to communicate with their customers. Website can also easily provide customers with more information about the products Ho and Wu (1999). However, customer has to be convinced that the information they obtain is high quality. Marketers should recognize the importance of the interface of communication with customers and wants to provide better design of interface for customers in order to obtain customer satisfaction (Shankar et al, 2003). In addition, Montoya-Weiss et al (2003) demonstrate that web site design characteristics affect customer evaluations of online channel service quality and risk, which in turn drive online channel use and customers‟ overall satisfaction with the service provider. As a result, the more interactive the website, the more satisfy the customer.
3. Trust (Only applicable for the online environment)
Customer satisfaction is closely related to interpersonal trust (Geysken et al, 1996; Ribbink et al., 2004) and is considered and antecedent of trust (Garbarino and Johnson,1999; Selnes, 1998). They state that customers‟ satisfactory experiences with firms are expected to increase consumers‟ willingness to make more purchase. According to Urban et al (2000) the most important element of trust is fulfillment. Trust is obtained by gathering expectations. They continue that, when commitments occurred, customer confidence raises in the belief that companies will also complete larger expectations. Online trust occurs because of the physical distance between the buyer and the seller, the absence of sales people, and the separation between buyer and product (Yoon, 2002). Lack of trust is frequently cited as a key reason why people do not make purchases online (Lee and Turban, 2001). However, awareness of the name of the company operating a web site and of its company‟s physical retail presence may develop web site trust (Yoon, 2002).
customer form perceptions about service attributes such as the reliability of information, availability of the website, and efficiency of transaction execution. Experience-based trust formation is also more liable because customers find it hard to set pre-consumption expectations of service quality in the online environment (Zeithaml et al. 2000). Hence, trust has a positive effect on customer satisfaction.
2.6. Conceptual Framework of the Relationship between Online/Offline Services and Customer Satisfaction
Based on the list of factors explained above, in this part we try to make a simple model on the determinants of customer satisfaction in offline and online services. The model will separate the common factors that influence customer satisfaction in both online and offline services, and specific factors which determine the customer satisfaction in online offline or offline service. The theoretical model of this study can be seen in the following figure:
Figure 1. Conceptual model of the online and offline medium in relation with customer satisfaction.
The figure above shows that there are 10 (ten) common factors determining customer satisfaction in online and offline environment which are, ease of obtaining information, frequency of use, prior experience, service recovery, diversity of products, technological system, attribute Performance, payment equity, disconfirmation of payment, brand image. Moreover,
3. Research Methodology
In the following part, the research design and further methodology is elaborated. Firstly, each stage of the research is described. Secondly, the population, sampling methodology, sample size and its representation are defined. Finally, the data collection process and further statistical analysis is discussed.
3.1. Research Design
This study will try to explore the determinants of customer satisfaction in online and offline medium. Then, we are going to compare the significance of those factors for customer satisfaction in online and offline medium. We focus on measuring the customer satisfaction in online and offline service, for instance in purchasing flight tickets either through online website or travel agent. The main reason we choose those sectors because consumers mostly using multichannel both online and offline service.
3.2. Population
The following research is targeting customers who have experiences in using online and offline channels for all sectors above. The population consists of friend lists in social network (e.g. facebook and mailing list)
3.3 Sampling Method
In order to obtain the data necessary to conduct this research, targeting sampling is employed. This procedure consists of a targeted selection of sample, at the convenience of the researcher. The sample will be restricted above 17 years old and have the experiences in both online and offline environments. To capture the demographical aspects, we target the samples which have a variety of nationality, geographic area, age, and education.
3.4. Procedure
This questionnaire consists of eighty questions which will be sent randomly to individuals across countries, namely The Netherlands, Indonesia, Japan, Australia, Germany, France, UK and Malaysia. Further, the questionnaire will be developed and administered by means of internet–based applications. The collection of data was made through the use of a personal questionnaire designed to answer the research question and sub-questions. In total, we received 110 responds from selected individuals from mailing lists and facebook, thus insuring for heterogeneity among respondents.
3.5. Questionnaire Design
In developing the questionnaire, an exhaustive research review was performed to identify validated scales for measuring the relationship between the common factors, specific factors(in online and offline) and customer satisfaction. However, some of the scales were later adapted to fit the context of the present study. The employed measurement scales can be found (Table 2).
The questionnaire consists of 80 questions. The questions are divided into three sections. First section consisted of 41 questions related to customer satisfaction in online; the second section involved 34 questions related to offline, the last section which consists of 5 questions related to personal information such as age, education, gender, countries and current location. The questions are aimed to measure the dependent variables: general satisfaction of customers in online and offline, and independent variables such as the common factors (frequency of service use, ease of obtaining information, prior experience, service recovery, diversity of products, technological system, attribute performance, payment equity, disconfirmation of payment, brand image) in online and offline, and the specific factors in online (interactivity of website, trust); specific factor in offline (personal interaction).
Table 2.
The Measurement of Variables and Sources
Name of the
variables Name of the construct Literature Scales
Online(common factors)
Ease of obtaining information
It is easy to get access to the company‟s website
The website provides relevant information that you need .
The information provided makes you easier to decide to purchase
The website reduces the searching time
Ribbink et al. (2004) Seven-point Likert
Frequency of Use
I often visit this airline‟s company website I often purchase plane tickets through this website
The more I visit this website the more I tend to purchase Gomez et al.(2006) And author modification Seven-point Likert Prior Experience
I had good experience with this airline company‟s website
I enjoy buying plane tickets in this website When I decide to purchase plane tickets, this company‟s website is my first choice My past experience in purchasing plane tickets through this website leads to more purchase in the future
(Rundle-Thiele, 2005)
and Caruana (2000) Seven-point Likert
Service Recovery
It is easy to get in contact with this airline company‟s website
This airline company quickly replies to complaints
This airline company is interested in feedback
Diversity of Products
This airline company‟s website has variety of services
The services displays are attractive. Ribbink et al.(2004)
Seven-point Likert
Technological System
I think this airline company‟s website has an advanced technology system
The technology provided makes the online purchase easier
(Ho and Wu, 1999) Seven-point Likert
Attribute Performance
This airline company‟s website sells services with high quality
This airline company‟s website provides convenient service through its site.
Montoya-Weiss et al. (2003)
Seven-point Likert
Payment Equity
The price of the tickets in the airline‟s website is often lower than in the travel agent
The airline company‟s website sometimes gives additional discount if you purchase through its website
The price of the tickets when you buy on the website is as fair as the service quality
Bolton and Lemon (1999) and Author
Modification Seven-point Likert
Disconfirmation of payment
The payment that you make when purchasing through airlines company‟s website is lower than you expected
The payment that you make when purchasing through airlines company‟s website is as you expected
The payment that you make when purchasing through airlines company‟s website is higher than you expected
Bolton and Lemon
(1999) Seven-point Likert
Brand Image
I am committed with certain brands airlines company when I purchase plane ticket online
Brand image of this airlines company affects my purchase decision when I purchase online
Online (Specific Factors)
Interactivity of Website
The airline company‟s website is enjoyable I think the airline company‟s website has a good design
I think the airline company‟s website is attractive and informative
In general, I am satisfied with the way that the airline company‟s website functions
Ribbink et al Seven-point Likert
Trust
I think this the airline company‟s website acts in my best interest
I think that the information offered by the airline company‟s website sincere and honest
This the airline company‟s website does not make false statements
I trust this the airline company‟s website I am willing to give my credit card number to this the airline company‟s website
I am willing to give private information to this the airline company‟s website
Gomez et al. (2006), Horppu et al. (2008),
Ribbink et al.(2004) Seven-point Likert
General satisfaction (online)
In general, how satisfied are you with the airline company‟s website
Bolton and Lemon(1999) Seven-point Likert Offline (common factors) Ease of obtaining information
It is easy to get access to the travel agent The travel agent provides relevant information that you need
The information provided makes you easier to decide to purchase
The information from the travel agent reduces the searching time (brochures, booklets)
Ribbink et al. (2004)
Seven-point Likert
Frequency of use
I often visit this travel agent
I often purchase plane tickets through this travel agent
The more I visit the travel agent the more I tend to purchase
Gomez et al (2006) and
Prior Experience
I had good experience with this airline travel agent
I enjoy buying plane tickets in the travel agent
When I decide to purchase plane tickets, this travel agent is my first choice
My past experience in purchasing plane tickets in travel agent leads to more purchase in the future (Rundle-Thiele, 2005) and Caruana (2000) Seven-point Likert Service Recovery
It is easy to get in contact with this airline travel agent
This airline travel agent quickly replies to complaints
This airline travel agent is interested in feedback
Ribbink et al.(2004) Seven-point Likert
Diversity of products
This airline travel agent has variety of services
The services displays in this travel agent are attractive.
Ribbink et al.(2004) Seven-point Likert
Technological system
I think the airline travel agent has an advanced technology system
The technology provided makes the purchase easier
(Ho and Wu, 1999) Seven-point Likert
Attribute Performance
This airline travel agent sells services with high quality
This airline travel agent provides convenient service Montoya-Weiss et al. (2003) Seven-point Likert Payment Equity
The price of the tickets in the travel agent is often lower than in the airline‟s website (you mentioned above)
Your travel agent sometimes gives additional discount when you purchase with them
The price of the tickets when you buy with the travel agent as fair as the service quality
Bolton and Lemon (1999) and Author
modification
3.6. Statistical Method
In order to answer the main questions of this study, empirically we will employ regression model. For the regression model, general customer satisfaction in online and offline service is as a dependent variable, while the other variables such as common factors and specific factors are as explanatory variables. The significance and the sign of coefficients explain the relationship between those variables and customer satisfaction. The magnitude of the coefficients can be used to measure the importance of those variables in affecting customer satisfaction.
To estimate the determinants of customer satisfaction in online service, this study estimates the following cross-section model:
Disconfirmation of payment
The payment that you make when purchasing through travel agent is lower than you expected
The payment that you make when purchasing through travel agent is as you expected
The payment that you make when purchasing through travel agent is higher than you expected
Bolton and Lemon (1999)
Seven-point Likert
Brand Image
I am committed with certain brands when I purchase in the airline travel agents
Brand image affects my purchase decision when I purchase in the travel agent
Horppu et al. (2008) Seven-point Likert
Offline Specific factor
Personal Interaction
The staff of this airline travel agent is competent and professional
I like the relationship that I have with the staff of this airline travel agent
The staff of this travel agent is friendly and helpful
Personnel are knowledgeable about the services they sell
Gomez et al (2006) Oliva et al. (1992) Seven-point Likert General Satisfaction (offline)
In general, how satisfied are you with travel agent?
Bolton and Lemon (1999)
16 16 15 15 14 14 13 13 12 12 11 11 10 10 9 9 8 8 7 7 6 6 5 5 4 4 3 3 2 2 1 1 0 X X X X X X X X X X X X X X X X Yi (1) Where:
Y: General customer satisfaction in online service, X1: Ease of Obtaining Information in online service
X2: Frequency of use in online service
X3: Prior Experience in online service X4: Service Recovery in online service X5: Diversity of products in online service X6: Technological system in online service X7: Attribute performance in online service X8: Payment equity in online service
X9: Disconfirmation of Payment in online service X10: Brand image in online service
X11: Interactivity of Website X12: Trust in online service
X13: Dummy Gender, in which Male= 1 and Female= 0
X14: Dummy Location, in which developed countries= 1 and developing countries = 0 X15: Education
X16: Age 0
: Intercept
1-16: Coefficients of independent variables
: Error term
The estimation model for customer satisfaction in offline service is expressed as:
Where:
Y: General customer satisfaction in offline service, Z1: Ease of Obtaining Information in offline service
Z2: Frequency of use in offline service
Z3: Prior Experience in offline service Z4: Service Recovery in offline service Z5: Diversity of products in offline service Z6: Technological system in offline service Z7: Attribute performance in offline service Z8: Payment equity in offline service
Z9: Disconfirmation of Payment in offline service Z10: Brand image in offline service
Z11: Personal interaction in offline service
Z12: Dummy Gender, in which Male= 1 and Female= 0
Z13: Dummy Location, in which developed countries= 1 and developing countries = 0 Z14: Education
Z15: Age 0
: Intercept
1- 15: Coefficients of independent variables : Error term
4. Results
4.1. Respondents Profile
The data set consists of 110 respondents from across countries who have experience in purchasing flight tickets via online and offline. The characteristics of respondents can be seen in table 3 below and the descriptions of the profile will be elaborated.
Table 3
The Profile of Respondents
Demographic Variable Sample (n=110)
4.1.1. Gender
As can be seen in table 3 above, the distribution of respondents participating in this research is nearly equally distributes; where 53.6% of respondents are male, while 46.4% are female.
4.1.2. Age
Most of the participants who have took part of this questionnaire are within the range 26-34 years old, accounting for 58.2% of the sample. The second largest age group covers 28.2% (35-43 years old), while a small group of respondents are at the age of 44 or older, standing for 7.3%. The smallest group scores 6.4% within 17-25 years old from the total respondents.
4.1.3. Education
Regarding to the level of education, remarkably the majority of respondents are in the level of higher education (Master/doctoral) which accounted for 73.6%. Then, it is followed by participants who have bachelor degree around 17.3%. The other consists of respondents who have a secondary degree, about 8.2% and a primary degree (0.9%).
4.1.4. Nationality
The results show that the largest part of participants has Indonesian nationality, which is for 97.3%. Meanwhile, the rest of respondents are from Polish, Dutch and Malaysian, and they are distributed evenly, at 0.9%. The lack of respondents from outside Indonesian nationality is because this questionnaire was distributed during summer holiday; hence most of them did not give responds.
4.1.5. Current Location
4.2. Data Descriptions
This part will explain about the score of variables which are probably determining the customer satisfaction in online and offline. The score of each variable is produced by taking the average score of all questions belong to certain variable for all respondents. The detail score of variables both in online and offline given by respondents will be explain in the following parts.
4.2.1. The Score of Variables in Online
The table 4 presents summary statistics of variables which shows the respondent rate on variables that probably determining customer satisfaction in online service. There are common factors and specific factors that influence customer satisfaction in online medium. With respect to the common factors, the ease of obtaining information, on average, the respondents give score 5.9 which is relatively much higher than its minimum score 1.5. This variable has the highest score compared to the score of the other variables. It indicates that online service provides relevant information and makes easier for customers to decide to purchase. Moreover, the respondents also assign a relatively high score to the other variables such as technological system, attribute performance, prior experience, diversity of products, and brand image. On the other hand, the disconfirmation of payment is given the lowest score, 3.9, which is much lower than the maximum score. It implies that the payment that the respondents make when purchasing online is higher than their expectation. In addition, in terms of specific factors, participants give high score to the interactivity of website which is 5.2 and it is considerably higher than the score minimum 1.5. It is also in line with trust factor, where respondents assign higher score 5.0 than its minimum 1.7. Highly average score in specific factors indicate that customers find the interactivity of website and trust is mainly important in online service. Customers have the propensity to share personal information or even their credit card numbers to the online service. Overall, customers are satisfied in using online service. It is showed by a relatively high score of general satisfaction in online service (5.4).
Table 4
Summary Statistics for Variables in Online
4.2.2. The Score of Variables in Offline
Similar to the online environment, there are also common factors and specific factors that affect customer satisfaction in offline medium. The table 5 shows that, with regard to the common factors, ease of obtaining information assign notably higher score 5.0 than its minimum score 1.5. It has also the highest score compare with the other variables in offline medium. It pointed out that in offline medium, likewise in the online, offers relevant information to customers and it makes customers easier in purchasing decision process. However, participants give relatively low score within the range 4.1 to 4.8 to the other common factors such as prior experience, service recovery, technological system, payment equity, disconfirmation of payment and brand image. The lowest average score belongs to the frequency of use, respondents give average score only 3.9 which is far behind the maximum score 7.0. This entails that the respondents do not use the offline service often when purchasing fight tickets; they also rarely buy flight ticket in offline medium. Furthermore, in offline specific factor which is personal interaction, participants assign
No. Online environment Mean Min. Max.
1. Ease of obtaining information 5.9 1.5 7.0
2. Frequency of use 4.7 1.0 7.0 3. Prior Experience 5.2 1.3 7.0 4. Service Recovery 4.7 1.7 7.0 5. Diversity of products 5.0 1.5 7.0 6. Technological system 5.4 1.5 7.0 7. Attribute performance 5.2 1.5 7.0 8. Payment equity 5.0 1.7 7.0 9. Disconfirmation of Payment 3.9 1.0 7.0 10. Brand image 5.0 1.0 7.0 11. Interactivity of Website 5.2 1.5 7.0 12. Trust 5.0 1.7 7.0
considerably higher score 4.9 than its minimum score 1.0. It implies that offline service provides more personal interaction to assist customers in choosing or purchasing the service. In general, the level of satisfaction of respondents in offline is above average where the score is 4.9. It is higher than the minimum score 1. Nevertheless, in comparison to online medium, participants in offline environment have lower satisfaction than in online medium. It is proved by the average score in general satisfaction offline is 4.9, while in online is 5.4.
Table 5
Summary Statistics for Variables in Offline
4.3. Estimation Results
This part presents the estimation results of the determinants of customer satisfaction in online and offline service. The model used for estimation is the cross-section model that has been presented in section 3.6. The simple linier regression model will be employed. The summary of estimation results will be explained in the two following parts. The detail outputs of SPSS software are presented in the appendix B.
No. Offline environment Mean Min. Max.
1. Ease of obtaining information 5.0 1.5 7.0
4.3.1. Estimation Results of Regression on Customer Satisfaction in Online Service
In order to estimate the determinants of customer satisfaction in online service, in column (1) of table 1, we consider the general satisfaction as a dependent variable and all common factors and specific factors as independent variables. Moreover, the demographical variables are also included in the estimation. In column (2) of table 6, the most insignificant variables based on their p-value in column (1) are excluded. Hence, in column (2) we included only the statistically significant variables which affect general customer satisfaction in online service.
In column (1) of table 6, all independent variables are included in the estimation. The result shows that the variable ease of obtaining information is statistically significant affecting general satisfaction in online services. The coefficient of this variable is significant at 5 % significant level with a positive sign. The positive sign implies that the easier to obtain information from online service could enhance customer satisfaction with the service as a whole. This result confirms the finding of previous studies such as Oliva et al. (1992) and Shankar et al. (2003). The ease of obtaining information consists of how easy to get access to website, whether the website provides relevant information, whether the information makes easier to decide to buy, and whether the website reduces the searching time.
The interactivity of website is also found to have a significant effect on general customer satisfaction in online service. The coefficient of this variable is statistically significant at 10% significant level. Moreover, the sign of the coefficient is positive. It can be interpreted that the more interactive the website, the more satisfied the costumers. This result supports the finding of Montoya-Weiss et al. (2003) which also come up with the same conclusion.
Table 6
Estimation Result of the Determinants of Customer Satisfaction in Online Service
Dependent Variable: General Satisfaction in Online
Independent Variables (1)
Include All Independent Variables
(2)
Include Statistically Significant Variables
Ease of Obtaining Information 0.214** 0.315***
(0.096) (0.072) Frequency of use -0.059 (0.075) Prior Experience 0.109 (0.087) Service Recovery -0.074 (0.085) Diversity of products 0.017 (0.087) Technological system 0.060 (0.100) Attribute performance 0.090 (0.099) Payment equity 0.065 (0.066) Disconfirmation of Payment -0.024 (0.045) Brand image 0.080 (0.050) Interactivity of Website 0.243* 0.396*** (0.128) (0.093) Trust 0.323*** 0.358*** (0.096) (0.083) Gender -0.188 (0.122) Location 0.235 (0.143) Education -0.167 (0.123) Age -0.120 (0.097) Constant 0.533 -0.268 (0.628) (0.316) Observations 110 110 R-Squared 0.802 0.772
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
payment, and brand image are not statistically significant at 10% significant level. In addition, the demographical variables such as gender, location, education, and age are also not significant in affecting the general customer satisfaction in online service. In general, the model in column (1) is good in fitting the data of customer satisfaction since the R-squared is relatively high.
In column (2) of table 6, we exclude the most insignificant variables in column (1) based on their p-value. Hence, in the end, in column (2) we include only the statistically significant variables affecting the general customer satisfaction in online service. The coefficient of variable ease of obtaining information becomes more significant at 1% significant level with a positive sign. The magnitude of its coefficient is 0.315. It means that a one-point increase in the score of ease of obtaining information will increase general customer satisfaction by about 0.315 points. The coefficient of interactivity of website is also more significant than that in column (1). The sign of coefficient is positive and the magnitude is 0.396. It indicates that a one-point increase in the score of interactivity of website will increase general customer satisfaction by about 0.396 points. The significance and the sign of trust‟s coefficient in column (2) is relatively stable. The main consequence of excluding some variables from the model is the value of R-squared decreasing.
4.3.2. Estimation Results of Regression on Customer Satisfaction in Offline Service
Table 7
Estimation Result of the Determinants of Customer Satisfaction in Offline Service
Dependent Variable: General Satisfaction in Offline
Independent Variables (1)
Include All Independent Variables
(2)
Include Significant Variables
Ease of Obtaining Information 0.213*** 0.253***
(0.076) (0.063) Frequency of use 0.066 0.094** (0.055) (0.041) Prior Experience 0.048 (0.077) Service Recovery -0.025 (0.088) Diversity of products 0.101 (0.078) Technological system 0.004 (0.057) Attribute performance -0.044 (0.089) Payment equity 0.175** 0.170*** (0.075) (0.058) Disconfirmation of Payment -0.030 (0.049) Brand image -0.076 -0.082* (0.049) (0.043) Personal Interaction 0.663*** 0.694*** (0.090) (0.067) Gender 0.005 (0.108) Location 0.125 (0.124) Education -0.007 (0.105) Age 0.038 (0.089) Constant -0.442 -0.359 (0.500) (0.224) Observations 110 110 R-Squared 0.873 0.868
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
