the intersection of hedonic and utilitarian values in … · . wang and scheepers (2003) studied...

THE INTERSECTION OF HEDONIC AND UTILITARIAN VALUES IN THE SUCCESS MODEL OF INFORMATION SYSTEM:

A STUDY ON SOCIAL MEDIA

GAFFAR HAFIZ SAGALA, AND SUMIYANA

Universitas Gadjah Mada

Abstract This study investigated the success of e-commerce using a new model that integrates the hedonic and utilitarian value in information systems. The new model was constructed based on the models created by Wang and Scheepers (2007) and Wang (2008). In other words, we modified and integrated both models into a new model. The results of the study suggest that the hedonic and utilitarian values could explain the behaviour of the user satisfaction and intention to repurchase. It means that the hedonic and utilitarian concepts improve the success model. Eventhough, the comparison shows that the utilitarian value-based model has a better goodness fit than that of the hedonic model, this study infers that both hedonic and utilitarian characteristic are actually not separated. We implies that all system developers should induce both hedonic and utilitarian contents into the systems they built. Keywords: hedonic value, utilitarian value, IS-success, user satisfaction,

user repurchase intentions, preposterous

SNA 17 Mataram, Lombok

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Introduction

Most research in information technology (IT) focus on productivity-oriented (task-

oriented) IT, such as processing and distribution. And so, only a few study investigate

entertainment-oriented IT, such as virtual world and social network (Holsapple and Wu,

2007). Heijden (2004) classifies the type of entertainment-oriented system as the

hedonic information systems (HIS). The research of HIS is still relatively new and is a

domain that has not been widely explored (Wang and Scheepers, 2012). Hedonic

information systems researchers are still discussing the acceptability (Gu et al., 2010;

Heijden, 2004; Holsapple and Wu, 2007; Wang and Scheepers, 2012). Heijden (2004)

published a research about hedonic conditions in information systems under limited

conditions which is important to be validated.

IT user is not a simple user, in many conditions they are also customers

(Holsapple and Wu, 2007). Wang (2007) found that the success of information system

related to behavioural intention is determined by the customer satisfaction and

perceived value. The customer satisfaction and perceived value are formed by the

information quality, system quality, service quality (DeLone and McLean, 1993; 2003;

Seddon, 1997), perceived ease of use, perceived usefulness (Davis, 1989; Davis et al.,

1989; Roca et al., 2006), confirmation (Roca et al., 2006), cognitive absorption,

personal innovativeness, and playfulness (Agarwal and Karahana, 2000).

Several studies have examined the integration of utilitarian and hedonic

characteristics in information systems. The studies were conducted to determine the

acceptability of electronic commerce (Childers , 1991), and to predict the behavioural

intention to continue the use (Gu et al., 2010; Venkatesh et al., 2012; Zhou et al., 2012).

However, these studies have not been able to provide a comprehensive understanding

regarding the success of information system.

Electronic trading is fully mediated by IT, including the use of social media and

virtual world (Wells et al., 2011). Many companies begin to use social media platforms

such as twitter, facebook, blogs, and forums-host client to communicate with their

customers (Culnan et al., 2010). In Indonesia some private enterprises (retailers) begin

to use social media to do their business. Some online companies also use social media

for advertising purposes (for example tokobagus.com and berniaga.com). The three-

dimensional virtual worlds (3DVWs), such as second-life, have allowed many

companies to perform their business and marketing activities under more complex

environments. Culnan et al. (2010) found that of social media users, 64% of them use


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social media for internal needs, 56% to communicate with customers, and 40% for

working with external partners or suppliers.

Holbrook and Hirschman (1982) explain the consumer behaviour from the

information processing and experiential perspective. In this case, the utilitarian value is

classified as processing of information while the hedonic value as experiential

perspective. The processing of information involves the left brain and the experiential

involves the right brain. This study sought to integrate the utilitarian and hedonic

characteristics in a framework of information system model, under assumption that the

individual needs are responded by the left and right brain. Therefore, each individual

has the utilitarian and hedonic needs. The integration of utilitarian and hedonic

characteristics in information system will encourage the efforts of extensive use of

information systems and the achievement of large market share for electronic trading

system.

Basically, information system has the hedonic and utilitarian characteristics that

are already attached. This study aimed to investigate the integration of hedonic and

utilitarian values into single success model of information system. This study

formulated six research questions, namely: 1) Does the hedonic value have a positive

effect on the flow experience? 2) Does the flow experience have a positive effect on the

user satisfaction? 3) Does the utilitarian value have a positive effect on the perceived

value? 4) Does the perceived value have a positive effect on the user satisfaction? 5)

Does the user satisfaction have a positive effect on the user repurchase intention? 6) Are

there any differences between the hedonic value and utilitarian values in explaining the

behaviour of user satisfaction and user repurchase intention?

Hedonic and Utilitarian Values

Hedonic consumption refers to the evocation of multisensory image, fantasy, and

emotion when using a product. The configuration of this effect is named the hedonic

response (Hirschman and Holbrook, 1982). The multisensory refers to the acceptance of

experience involving multiple sensory modalities including taste, sound, scent, tactile

and visual images (Hirschman and Holbrook, 1982). Consumers researchers typically

consider this experience as afferent (e.g., a product taste test). Hedonic perspective

argues that afferent receiving multisensory impulses is an important form of consumer

response (Berlyne, 1971, in Hirschman and Holbrook, 1982). Individuals not only

respond to the multisensory external stimuli (e.g.: perfume) by encoding the sensory


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input, but also by producing multisensory images. For instance, smelling a perfume can

cause consumers just to not sense and encodes a scent, but also to produce internal

images featuring the sight, sound and tactile sensations, all of them are “experience“

(Hirschman and Holbrook, 1982).

The dominant goal of a system design is to encourage a long-term use of the

system (Heijden, 2004).The nature of the system, hedonic and utilitarian, can be viewed

as a tactic employed by the system developers to encourage the system use. The tactic

consists of the inclusion of hedonic content, such as animation, colour focus, sound,

interesting and aesthetical visual layout. The hedonic value of the system will generate

pleasant experience recieved by a user when using the system.

Hedonic perspective does not intend to replace the traditional consumption

theory, but rather to expand and improve its application (Hirschman and Holbrook,

1982). A product offers hedonic and utilitarian benefits to its consumers in the forms of

enjoyment experience or practical functions. Similarly, consumers have hedonic and

utilitarian purpose in their consumption activities (Hirschman and Holbrook, 1982;

Chitturi, 2008; Botti and McGill, 2010; Gu et al., 2010; Sindhav and Adidam, 2012;

Alex and Joseph, 2012; Ozen and Kodaz, 2012). Wu and Lu (2013) describes that the

hedonic value refers to the intrinsic motivations, while the utilitarian value to the

external motivations, that drive users to use the information systems. Intrinsic

motivation emphasizes on reasons that are controlled by experience, inherent in the

activity, and closely related to the individual’s interests (Wu and Lu, 2013).

The integration of hedonic and utilitarian characteristics in explaining the

consumers’ behaviour to purchase in electronic commerce has been conducted by

Childers (2001). Childers (2001) described the hedonic characteristics using the

perceived enjoyment and perceived ease of use, and described the utilitarian

characteristics using the perceived usefulness. Other information systems literature

explain that the perceived usefulness (utilitarian characteristics) and perceived

enjoyment (hedonic characteristics) are important antecedents for technology

acceptance (Wu and Lu, 2013). Social virtual service research also explained that the

hedonic and utilitarian values generate the affective commitment. In the end, the

affective commitment produces the intention to continue the use of social virtual service

(Zhou et al., 2012). Venkatesh et al. (2012) also stated that hedonic motivation

contributes to the behavioural intentions. Based on previous theory and research,

integrated hedonic and utilitarian values can function to stimulate a pleasant response.


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Wang and Scheepers (2003) studied the emotional response and imaginal

response as the main predictor of intention to use HIS that is mediated by flow

experience. Emotional response refers to the individuals’ various feelings in experience

when interact with hedonic information systems. While the imaginal response was

refers to a psychological state. Hedonic information system users can escape or distract

themselves from unpleasant event or self projection into a certain role and character.

Imaginal response and emotional responses that adopted by hedonic theory is used to

reflect the hedonic information systems users that also acts as a hedonic consumers

(Wang and Scheepers, 2012).

Wang and Scheepers (2012) used PAD (pleasure, arousal, and dominance)

theory (Mehrabian and Russell, 1974) to explain emotional response, since they argued

that PAD theory can explain emotional responses more comprehensively. Several

previous studies that examined the impact of emotional responses on information

system acceptance used only one dimension of emotional response, such as enjoyment

(Heidjen, 2004), playfulness (Chung and Tang, 2004), or fun (Okazaki, 2007).

Therefore, it is necessary to investigate the emotional response using all dimensions

(Wang and Scheepers, 2012).

According to Wang and Scheepers (2012), using PAD emotional model

constructed by Mehrabian and Russell (1974), argued that affective responses to the

environment can be explained by three variables, namely the pleasure, aurosal, and

dominance. The pleasure is verbal expression when respondents reported feeling happy

as opposed to unhappy, pleased as opposed to annoyed, satisfied as opposed to not

satisfied, pleased as opposed to melancholy, hope as opposed to despair, and relaxed as

opposed to bored. The arousal is measured by verbal reaction to environment (e.g.,

excited as opposed to relaxed, eager as opposed to calm, frenzied as opposed to calm,

restless as opposed to dull, awake as opposed to drowsy and provoked as opposed to not

feel anything). Mehrabian and Russell (1974) stated that the quality to arise an

environment is to improve the novelty, complexity, intensity, understanding,

impossibility, change, mobility or uncertainty of regulation (Foxall and Greenley, 1998;

Foxall and Yani-de-Soriano, 2005; Wang and Scheeprs, 2012). The dominance is

shown by the users reported feelings of control as opposed to being controlled,

influential as opposed to being influenced, in control as opposed to being treated,

important as opposed to fascinated , dominant as opposed to defeated, and autonomous

as opposed to guided.


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Extrinsic motivation is related to a variety of behaviours that made for reasons

beyond those inherent in the activity itself (Wu and Lu, 2013). In a job, this can be

illustrated by working for a salary. Work is an activity carried out by individual, while

salary is something beyong the job, but the salary is used as a reward to motivate

individual work quality. Within the context of interaction with HIS, though the use of

HIS is initiated by intrinsic motivation, yet the achievement of external objectives

depends on the interaction with the system itself (Childers et al., 2001, Heidjen, 2004;

Wang and Scheepers, 2012). The quality of individual interaction with the system

would be closely related to system quality itself. Referring to above questions, the

information systems success model of DeLone and McLean (1992, 2003) are suited to

explain the utilitarian characteristics in information system.

Perceived Quality

The purpose of utilitarian information system is to improve the performance and

efficiency of users’ job. Therefore, the developers align the functions of system with the

job requirements, and give as little as possible disruption to help users perform their

duties. The dominant goal of the design is productive use (Heijden, 2004). In utilitarian

perspective, the consumers concerned to buy the product in efficient way and timely to

achieve their objectives with minimal irritation levels (Childers, 200). That of course

requires good quality of information systems.

Internet is a phenomenon of communication and information system that is

suitable for DeLone & McLean Information System Success Model measurement

framework that built on communication theory (e.g., Shannon and Weaver, 1949)

(DeLone & McLean, 2003). In e-commerce, the main users are customers or suppliers

of the internet users. The customers and suppliers use the system to buy or to sell and to

conduct business transactions. Electronic trading decisions will have an impact on

individuals users, organizations, industries, economy and even national. This process of

communication and trade is match with updated DeLone & McLean Information

System Success Model with its six dimensions of success (DeLone & McLean, 2003).

Several studies have developed and validated information system success model

of DeLone & McLean (1997; 2003) which refers to users’ perception on the information

quality, system quality, and service quality as the antecedents of information systems

success (DeLone & McLean, 1992; Seddon, 1997; DeLone & McLean, 2003; Wang,

2008). The system quality is described as the presence or absence of errors in the


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system, the consistency of user interface, the ease of use, the quality of documentation,

and sometimes the quality of program code maintenance (Seddon, 1997).

The information quality is related to issues such as the relevance, timeliness, and

accuracy of information generated by information system. However, not all IT

applications involve the production of information for decision making. For example,

word processing does not actually produce the information, so that the information

quality does not measure all things that can be applied to systems (Seddon, 1997).

Meanwhile, the service quality is an overall support delivered by the service provider,

regardless of whether the support is delivered by information systems department, new

organizational unit, or outsourced to an internet service provider (ISP). The importance

of service in this case is greater than ever. This is caused by the fact that the current

users are also the customers, so that the poor user support will mean the lost of

customers and sales (DeLone & McLean, 2003). In the e-commerce environment, the

impact of the web site design on customer purchases can not be understood without

evaluating the usability of web sites and relevance of information provided to

prospective buyers for a purchase decision (DeLone & McLean, 2003).

Flow Theory

Flow metaphor is one way to describe the actions in the moments that stand out as the

best part of life. Athletes describe it as “in the zone," a religious mystics describe it as

"ecstasy", artists and musicians describe it as "aesthetic" (Csikszentmihalyi, 1997).

Csikszentmihalyi (1975) describes this flow as the holistic sensation that people feel

when they act with total involvement. Meanwhile, Nah et al. (2011) defines flow as an

optimal state of experience, in which individuals are actually absorbed and involved in

activities that no other problems appear.

Siekpe (2005) explained when consumers shop at brick and mortar store, they

have opportunity to explore the aisles and check the product carefully and meticulously.

This experience may be enhanced by sensory stimulation with colourful displays,

ambient music, tempting aroma, products physical examination, and interaction with the

sellers or other customers. However, online shopping does not have such real

experiences, but rather produce convenience, cost and time savings.

Online consumers not only act as an ordinary buyer, but also as a computer user

(Siekpe, 2005). According Siekpe (2005), several retailers have created a world wide

website (www) which provides information to the user, ranging from store sites to sales


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promotions, either to employment opportunities and product catalogue. The main

problem is not easy to build a website that is entertaining and interesting for visitors and

enabling them to interact, so visitors can complete a shopping transaction without

frustration. In an effort to build a website that attracts visitors to do electronic shopping,

flow merging has been proposed as one of the concepts to improve the quality of the

visitor experience when doing electronic shopping transactions. So that, flow

experience can cause repurchase intention, or back to the same site in the future

(Siepke, 2005).

Individual's involvement in a forum can give the sensation of flow, when the

forum is in accordance with the individual’s desire and ability. Thus, the individuals

will get the value they want in the engagement. Nah et al. (2011) explain that the

individuals’ involvement in virtual world will make them feel as being a part of the

virtual world, which makes them feel to have a role. And this can cause the flow

experience to them. Wang and Scheepers (2012) stated that the flow is very suitable to

describe the emotional involvement and experience absorption in playing activities.

Wang and Scheepers (2012) use the enjoyment, concentration, and time distortion as the

dimensions of the flow. The enjoyment reflects the user’s happiness and pleasure when

they interact with HIS. The concentration refers to the experience of total involvement

in the interaction that demands the other important attentions are overlooked. The time

distortion refers to the inability to realize the passage of time while interact with HIS.

Flow experience can be achieved when user skills match the challenges

presented by the system (Ghani and Deshpande, 1994; Wang and Scheepers, 2012).

Online flow may have consequences on improving learning, perceived behavioural

control, exploratory behaviour, positive subjective experiences, and distortions in time

perception (Novak and Hoffman, 1996). Overall, flow can affect the patterns of

navigation and repeated visits to a commercial sites (Siekpe, 2005).

Electronic Commerce and Social Media

IT-mediated e-commerce offers numerous organizational advantages (e.g., access to

more consumers, increased availability and accessibility information), but also comes

with some inherent challenges (Wells et al., 2011). The application of social media such

as twitter and facebook creates new opportunities for companies to improve their

internal operations and help the companies to collaborate in new ways with customers,

business partners, and suppliers (Culnan et al. 2010). IT does generally have a dramatic


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impact on business operations (DeLone & McLean, 2003). The press gave a lot of

anecdotal evidence of the adoption of social media by a variety of companies ranging

from Fortune 500 to small businesses greatly. Achieving and measuring the business

value of social media continues to be a challenge for many organizations. However,

despite the wide spread use of social media, many respondents still report that the

applications have not provided measurable benefits when used either internally or by

customers and business partners (Culnan et al. 2010). DeLone & McLean (2003)

explains that the company made a large investment in e-commerce applications but are

sometimes difficult to assess the success of their e-commerce systems (DeLone &

McLean).

According to Culnan et al. study (2010), which is based on the analysis of the

use of four most popular social media platforms to interact with customers, i.e.: twitter,

facebook, blogs, and client-hosted forum, how people use these platforms varies

according to industry. He argues that in order to get the value of social media, the

companies need to develop an implementation strategy based on three elements:

voluntary adoption, community building, and absorptive capacity.

Holsapple and Wu (2007) indicate that the users of virtual world or social media

are hedonic individuals. Meanwhile, Gu et al. (2010) classify students as users of IT for

hedonic purpose and business workers as users of IT for utilitarian purpose. Instant

Messaging (IM) is used by teenagers for social entertainment, especially informal

conversation and socialization, such as the daily chat and event planning (Grinter &

Paylen, 2002; Lenhart et al., 2001; in Gu et al., 2010). While for the adults, IM is used

for work-related communication, because IM is a tool that supports the spontaneous and

opportunistic communication among colleagues and real-time communication with

customers for work-related projects (Huang & Yen, 2003; Gu et al., 2010).

This becomes a very interesting to be studied since the development of e-

commerce are no longer just based on HIS company website, but also began to extend

toward social media-based e-commerce. The achievement of voluntary adoption,

community development, and absorptive capacity make the companies’ competitive

advantage must meet the needs of user perception. Therefore, a comprehensive study is

needed to determine the indicators of the users’ needs fulfilment of e-commerce

information systems. On social media platforms, there are two characteristics of user;

the ones that using IT for entertainment purposes (hedonic) and productive purposes

(utilitarian) for the others.


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Framework

This study considers the hedonic and utilitarian needs as the antecedents of information

systems success. This study used the emotional and imaginal response to describe the

hedonic value in information systems. The information quality, service quality, and

system quality are variables to describe the utilitarian value. Wang and Scheepers

(2012) using the flow experience as mediating variables between the hedonic value and

usage behaviour. Meanwhile, the user satisfaction is an important variable to measure

IS-success (DeLone and Mclean, 2003; Seddon, 1997; Wang, 2007). Accordingly, this

study uses the flow experience as variables to mediate the causal relationship between

the hedonic value and user satisfaction.

Customers who have first hand experience with electronic trading systems may

create values and satisfaction when using the system, but not in the pre-use situations

(Wang, 2007). Accordingly, the intention to re-use in the future is more appropriate to

measure the information system success (Wang, 2007). Therefore, this study uses the

repurchase intention as endogenous variables. The research model can be seen in the

figure below.

Figure 1. Research Model

The research model was developed based on two basic models from previous

studies (Wang, 2008; Sceepers and Wang, 2012). This study was attempting to explain

the behaviour of information systems users when doing electronic shopping via social

media. Social media platforms are selected to capture the phenomenon of many

corporate and individual traders who trade in social media or virtual world (Culnan et

H1

H2

H3H6

H5

H4H7

H8

H10

H9

Emotional response

Imaginal response

Repurchase Intention

Satisfaction

Flow Experience

Perceived Value

Information quality

System quality

Hedonic value

Utilitarian Value

Service quality


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al., 2010). The comparisons of both basic models were aimed to gain a deeper

understanding about those basic models. The results of the comparisons can be used in

the formulation consequential model best suited for information systems users in the

context of social media-based e-commerce. Furthermore, consequences and

appropriateness of the model can be useful to get the right composition in the system

design and decision-making for electronics-store owner to utilize the users’ behaviour

on doing deals and sales to achieve business goals. Both models are depicted as follows.

Figure 2 Hedonic value-based model

Adapted from Wang and Scheepers (2012)

Gamb

2.3.

Figure 3 Utilitarian value-based model

Adapted from DeLone and McLean (2003)

Hypothesis Formulation

Emotional response is reflected in hedonic customer consumption and behavioural

intention (Wang and Scheepers, 2012). On the other hand, satisfaction is an important

factor that affect the use of system. The user satisfaction also has the most powerful

individual direct effect (Igbaria and Tan, 1997). However, Wang and Scheepers (2012)

have not been able to deduce the effect of emotional responses to the satisfaction.

Repurchase

Satisfaction Perceived Value

Information quality

System quality

Service quality

Emotional response

Imaginal response


Satisfaction

Flow Experience


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Emotional response causes the feeling of pleasure that stimulates the individual to

preserve in the happy situation, it refers to the flow experience proposed by

Csikszentmihalyi (1975). Various feelings of pleasure experienced by user when using

the information system will build the satisfaction in him, and so the hypothesis is

formulated as follows.

H1: The emotional response has a positive effect on the flow experience.

H2: The emotional response has a positive effect on the satisfaction.

Flow is strongly associated with imaginal responses (projection of roles and

escapism). When individuals think that when the hedonic information systems allowed

them to escape from the real world or to project themselves into certain roles, they are

more likely undergo the flow experience and absorption or being lost in the system

(drift) (Holsapple and Wu, 2007; Wang and Scheepers, 2012). That feeling would build

perceived benefits on the users’ hedonic value (Zhou et al., 2012) as the users’ hedonic

needs fulfilment. It also has implications to the satisfaction, since the users get in the

situation that is in accordance with their expectations (projection of role and escapism).

This study formulates hypotheses as follows.

H3: The imaginal response has a positive effect on the flow experience.

H4: The flow experience has a positive effect on the user satisfaction.

In this case, the users will feel that the information system is valuable if they

could find the information they need easily and without excessive cost. The cost refers

to money, time, or other sacrifices. Information system service can quickly resolve the

problems that occur in the system. However, in service quality context, the devices that

are used must support the service that being used by individuals. Complete information,

easy and quick access, and other supportive information will meet the users’ productive

needs. When the users’ needs were accommodated just as they expected and the related

costs (monetary/non-monetary) were deemed appropriate, the users feel happy and

satisfied. This study formulated the following hypotheses:

H5: The information quality has a positive effect on the perceived value.

H6: The system quality has a positive effect on the perceived value.


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H7: The service quality has a positive effect on the perceived value.

H8: The perceived value has a positive effect on the users satisfaction.

A higher level than the acceptability of use is the intention to reuse. Several

studies have examined the association of the perceived value and satisfaction with the

intention to use, intention to repurchase, or intention of sustainable use (DeLone &

McLean, 1992; Seddon, 1997; DeLone & McLean, 2003; Wang, 2007; Wells et al.

2011; Roca et al., 2006; Zhou, 2012). The perceived value refers to the costs and

benefits incurred by users of e-commerce information systems (Wang, 2007), that will

stimulate the feeling of appropriate or lucky for electronic commerce information

systems user. The satisfaction refers to the consumers’ feeling of benefit from certain

services (Kim and Son, 2009; Zhou, 2012). Furthermore, the feeling of appropriate

stimulates the consumers to buy again. Consistent with previous studies, this study

formulated hypotheses as follows.

H9: Perceived value has a positive effect on the repurchase intention.

H10: User satisfaction has a positive effect on the repurchase intention.

Population and Sample

This study population is the users of popular social media in Indonesia, i.e. Facebook,

Twitter, Kaskus, and BlackBerry Messenger (BBM), as those platforms are most

demanded by social media users in Indonesia. From the social media users, the users

who have ever did the online shopping with one of the existing electronic traders in

social media. The sample was taken using a snowball technique.

Data Collection Method

Data for all variables of this study were collected using a questionnaire in survey

method. The survey is a measurement process that is used to collect information in a

structured interview, with or without the interviewer (Cooper and Schindler, 2011). The

survey was conducted by issuing an electronic questionnaire on online store social

media page. The users have the freedom to choose to be respondents or not. The data

collection was also conducted using paper questionnaires. The questionnaires were put

in the place where the goods from online store are taken by the costumers who choose

cash on delivery shopping methods. The customers also have the freedom of choosing


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to be respondents or not. The consistency of the results of electronic and paper

questionnaires will be tested by the sensitivity test.

This research instruments were adapted from Wang and Scheepers (2012) and

Wang (2007). The instruments adapted from Wang and Scheepers (2012) are used to

assess the variables of emotional response, response imaginal, and flow experience. The

instruments adapted from Wang (2007) are used to assess the variable of information

quality, system quality, service quality, perceived value, user satisfaction, and

repurchase intention. These instruments were used with hopes that the questionnaires

have the data reliability and are able to translate the phenomena as the researcher’s

expectations. These instruments were used becuase their validity and reliability had

been verified in previous studies. The instruments were established in Likert scale (7

scales) like the previous studies.

Research Variables and Operational Definition of Variables

Operational definition is the attachment of a meaning into a construct or variable by

specifying the activities or actions to measure the variable (Kerlinger, 2006). Research

variables and operational definitions of the variables of this study can be reviewed in

Table 1.

Table 1. Research Variables and Operational Definitions

No Variable Operational Definition 1. Emosional

Response Various feelings in the experience when interacting with social media while doing electronic shopping (Wang and Scheepers, 2012)

2. Imaginal Response

Psychological state in which social media user distract himself from unpleasant events into a particular role or character when doing electronic shopping (Wang and Scheepers, 2012)

3. System Quality

User perception of the consistency of user interface, ease of use, and quality of documentation when doing electronic shopping in social media (Seddon, 1997)

4. Information Quality

User perception about relevance, timeliness, and accuracy of information generated by electronic commerce information systems in social media (Seddon, 1997)

5. Service Quality

User perception of the overall support delivered by service provider when doing electronic shopping in social media. Regardless of whether this support is delivered by the department of information systems and electronic traders on social media pages (Seddon, 1997)

6. Flow Experience

Holistic sensation felt by user when subjected to total involved in the use of social media when shopping online (Csikszentmihalyi, 1975)

7. Perceived Value

User perceptions of information systems in assessing effort to do compared to the benefits derived from the use of information systems when doing electronic shopping in social media (Wang, 2008)

8. User Favorable feelings towards consumer services obtained from the use of


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Satisfaction social media when doing electronic shopping (Wang, 2008) 9. Repurchase

Intention Encouragement of individuals to re-do the electronic shopping via social media (Wang, 2008)

Convergent Validity Test

The convergent validity tests employed in this study comprise of the analysis factor and

average variance extracted (AVE). Factors loadings indicate a convergence for all latent

constructs. All factor loading should be 0.5 or higher, 0.7 or higher is preferable

(Fornell and Larcker, 1981; Hair, 2010; Hartono, 2012). The results shows that the

entire values of factor loading (see Appendix 2 for detail) are more than 0.50, except

RESIM3 (0.00) and RESEM3 (-0.05). Consequently, both variables were excluded for

the further analysis.

AVE measurement of each construct was made to increase the confidence of

convergent validity of the constructs in this study. Hair et al. (2010) claim that if the

AVE values were above 0.5 then the construct has good convergent validity. The AVE

measurement results (Appendix 2) show that the validity of this study has good

convergence, since the overall value AVE constructs was above 0.5.

Reliability Test

This study examined the consistency of internal reliability using cronbach’s alpha

measure. The consistency of internal reliability has a maximum value of 0.6 (Malhotra

& Galleta, 2005; Malhotra, Kim and Agarwal, 2004; Sumiyana, 2007). The lowest

value of Cronbach’s Alpha for each variable is this study is still more than 0.6, as

presented in appendix 2. The results show that two items from the questionaire need to

be excluded before the subsequent data analysis. The excluded items consist of the

imaginal response item number 3 with a Cronbach 's Alpha value of 0.864 (above

0,787) and emotional response item number 3 with the value of Cronbach 's Alpha of

0.942 (above 0.910). These results are consistent with the previous convergent validity

results, which shows that the both items do not correspond to their latent constructs.

Discriminant Validity Test

Discriminant validity indicates the extent to which a construct is trully distinctive from

other constructs (Hair et al., 2010). High discriminant validity provides evidence that a

construct is unique and is able to capture some phenomena other measure are not (Hair

et al., 2010). The test consists of the confirmatory factor analysis (CFA) and average


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variance extracted (AVE). Confirmatory factor analysis (CFA) was used to confirm that

the constituent items are communed into the appropriate construct. The results showed

that the communality factor numbers are above 0.7. It can be concluded that the

discriminant validity was proven (Malhotra & Gulleta, 2005; Malhotra, Kim and

Agarwal, 2004; in Sumiyana, 2007). The CFA test results showed that all question

items were communed into nine factors and the variables were consistently valid.

Appendix 3 shows in detail that items question were communed into one construct with

the lowest value of 0.61 and the highest value of 0,94.

The second way to assess the discriminant validity is comparing the average

variance extracted (AVE) of a construct with the square of correlations of the construct

with other constructs in the model. According to Hair et al. (2010), an AVE value of a

construct that was higher than the square of correlations of the construct with other

constructs in the models shows good discriminant validity. Appendix 4 shows the AVE

values in the diagonal position of the table, and the square of correlation of a construct

with the others were shown below the each AVE values. The results show that each

construct has the AVE value which is higher than the square of correlation value of the

construct with the others. Thus, we can conclude that the indicator items used in this

study met the criteria of discriminant validity. The results of discriminant validity test

were summarized in Table 2 as follows.

Table 2. Discriminant Validity Test

Methods Results Conclusions

CFA CF value > 0.7 Valid

AVE r2 values < AVE

values

Valid

Results of Data Collection

From the electronic questionnaire which were published in social media, 128 responses

were obtained, although 6 response of them contained errors due to lack of response or

double responses from single respondent. Meanwhile, from 200 non-electronic

questionnaires, 155 responses were filled, and the all responses can be used, so we get a

total of 277 responses. The demographics characteristics describe the sample by age and

sex. The demographics characteristics of the obtained sample are as follows. Male

respondents number 99 people (35.74 percent) and female participants number 178


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people (64.26 percent). Most respondents are 21-25 years old, while the least are older

than 40 years old. The demographics characteristics are displayed in detail in Table 3 as

follows.

Table 3. Demographic Characteristics of Respondents

Indicators Sum Percentage Age

15-20 years 71 25,70% 21-25 years 105 37,90% 26-30 years 53 19,10% 31-35 years 31 11,20% 36-40 years 9 3,20% > 40 years 8 2,90%

Total 277 100% Sex

Male 99 35,74% Female 178 64,26%

Total 277 100% Data collection

Electronic questionaire 122 44,04% Non-electronic questionaire 155 55,96%

Total 277 100%

Descriptive Statistics

The value of each variable were obtained from the average value of its constituent

items. These average values were then used for further analysis. Table 4 below presents

the results of descriptive statistics including mean, median, minimum, maximum, and

standard deviation values for each variable. The results show that all variables have

good distribution since all their values of standard deviation are less than their mean

values.

Table 4. Descriptive Statistics

No. Variable

Mean Median Min. Max. Std.

Deviation1. RESEM 4.64 4.66 1.83 6.66 0.99 2. RESIM 4.89 5.00 2.44 6.88 1.01 3. FLOWEX 4.49 4.77 2.33 7.00 1.03 4. INFQU 4.74 5.00 2.25 7.00 1.13 5. SYSQU 4.65 4.80 1.20 7.00 1.22 6. SERQU 4.44 4.33 1.00 7.00 1.32 7. PERVAL 4.68 5.00 1.00 7.00 1.33 8. SATIS 4.86 5.00 2.00 7.00 1.15 9. INREUSE 4.93 5.00 1.66 7.00 1.08 Valid N (listwise) 277


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Hypothesis Testing

This study used the Structural Equation Modelling (SEM) for hypothesis testing. The

SEM is built on the foundation of two familiar multivariate techniques that are

multivariate regression and factor analysis (Hair, et al., 2010; Gudono, 2010). The SEM

is relatively more dynamic and sophisticated analysis for suitability models than

regression (Animesh, et al., 2011). The estimation technique of the structural equation

model used in this study is the Maximum Likelihood Estimation (MLE). The MLE is a

flexible approach for parameter estimation in terms of the parameter value with the best

possibilities to get the best suitability model (Hair, et al., 2010). The estimation used the

help of a statistical analysis software called LISREL 8.8. It is a flexible program and

can be used in some situations (cross-sectional, experiment, quasi-experimental, and

longitudinal studies). The model have both positive and negative determination

relationships (Sumiyana, 2007). This is shown by the coefficients of relationship path of

each variable and its t value. The path coefficients values and their t values can be seen

in the following table.

Table 5. Hypothesis testing

No. Hypothesis Coef t-stat Status

1. Hypothesis 1 (+) E. Response Flowex (ρx6x1) 0.76 9.29 Supported 2. Hypothesis 2 (+) E. Response Satis (ρx8x1) 0.003 0.03 Not supported 3. Hypothesis 3 (+) I. Response Flowex (ρx6x2) 0.07 1.02 Not supported 4. Hypothesis 4 (+) Flowex Satis (ρx8x6) 0.45 5.78 Supported 5. Hypothesis 5 (+) Infqu Perval (ρx7x3) 1.04 9.19 Supported 6. Hypothesis 6 (+) Sysqu Perval (ρx7x4) 0.73 5.18 Supported 7. Hypothesis 7 (+) Serqu Perval (ρx7x5) -0.87 -6.14 Not supported 8. Hypothesis 8 (+) Perval Satis (ρx8x7) 0.75 9.62 Supported 9. Hypothesis 9 (+) Perval Reint (ρyx7) -0.31 -3.47 Not supported

10. Hypothesis 10 (+) Satis Reint (ρyx8) 1.06 8.82 Supported

The results of SEM mesurement show that the influence of emotional response

to the flow experience has a coefficient of 0.76 and t value of 9.29 that is statistically

significant, and so the hypothesis H1 is supported. It can be concluded that the

emotional responses have a role in the achievement of the flow experience for the

individuals who perform electronic shopping via social media. On the other hands, the

influence of emotional responses to the user satisfaction is positive insignificant, since it

has the coefficient of 0.003 and the t value of 0.03 that is not statistically significant.

Accordingly, the hypothesis H2 is not supported. Higher emotional responses do not

increase the user satisfaction.


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The imaginal response does not show significant effect to the flow experience

with path coefficients value of 0.07 and t value of 1.02 that is not statistically

significant, and so the hypothesis H3 is not supported. The imaginal response has not

been able to make the users reluctant when doing electronic shopping via social media

to quickly move from the online store. The flow experience affect significantly the user

satisfaction, with the path coefficient value of 0.45 and a t value of 5.78 that is

statistically significant, and consequently, the hypothesis H4 is supported. It means that

higher flow experience when doing online shopping via social media will result in

higher level of users satisfaction.

The information quality has a significant effect on the perceived value. The path

coefficient of information quality is the biggest compared to that of the system quality

and service quality. The information quality to the perceived value has a path

coefficient value of 1.04 and a t value of 9.19 that is statistically significant, therefore

the hypothesis H5 is supported. This suggests that the information quality has an

important role in the formation of perceived value of online store information systems.

The path of system quality to the perceived value has a coefficient value of 0.73 and a t

value of 5.18 that is statistically significant, so the hypothesis H6 is supported. The

system quality significantly affects the perceived value. Therefore, the system quality

has a role in the formation of perceived value when the customers did online shopping

in social media. The path of service quality to the perceived value is known to have a

coefficient of -0.87 and a t value value of -6.14 that is not statistically significant, so the

hypothesis H7 is not supported. This suggests that the service quality does not affect the

perceived value. The path of perceived value to the user satisfaction showed a

significant effect of coefficient value of 0.75 and a t- statistic of 9.62 that is statistically

significant, and accordingly, the hypothesis H8 is supported. This suggests that the

higher perceived value of the individual will yield in a greater satisfaction to the users.

The path of perceived value to the repurchase intention have a coefficient of -

0.31 and a t value of -3.47 that is not statistically significant, then hypothesis H9 is not

supported. Meanwhile, the user satisfaction showed a positive and significant effect to

the repurchase intention. It is shown by the value of path coefficient that is 1.06 and a t

value that is 8.82 that is statistically significant, so the hypothesis H10 is supported.

This suggests that higher user satisfaction will be achieve higher repurchase intention in

the online shopping via social media. The not supported hypothesis H9 and supported

H10 suggests that the user satisfaction becomes an important pathway in the


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development of repurchase intention. The overall results of the hypotheses examination

can be illustrated by the following figure.

Figure 1. Result Model

Goodness-of-Fit Model

After the calculation of model estimation, the validity and reliability of the

model is tested. The model’s validity depends on the acceptance level of the model’s

goodness-of-fit (GOF) and the validity of the constructs used. The GOF indicates how

well the model and is evaluated by the covariance matrix among the indicator items

(Hair et al., 2010). The results of GOF analysis are presented in Table 6 as follows.

Table 6. Goodness-of-Fit Measurement

GOF Index Indicators Estimation Status Chi-Square Small score (p>0.05) 1868,27 (p=0.00) Low Fit NCP, Interval Small score, narrow

interval 942,62, (825,01; 1067,95)

Medium Fit

RMSEA, p(close-fit)

RMSEA ≤ 0,08 (p≥0.50) 0.064 (p=0.00) Good Fit

ECVI Small score, close with ECVI saturated

M*=7,23, S*=6,86, I*=136,51

Good Fit

AIC Small score, close with AIC saturated

M*=1994,62, S*=1892.00, I*=37675.42

Good Fit

CAIC Small score, close with CAIC saturated

M*=2484,76 S*=6266,32, I*=37874,25

Low Fit

NFI Above 0.90 0.95 Good Fit NNFI Above 0.90 0.97 Good Fit

0,07

0,76

0,45

0,75

1,04

0,003

0,73

-0,87

1,06

-0,31

Emotional response

Imaginal response

Information quality

System quality

Service quality

Flow Experience

Perceived Value

Satisfaction Repurchase Intention

1,00

1,00

1,00

1,00

1,00

1,00

1,00

1,00

1,00


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CFI Above 0.90 0.97 Good Fit IFI Above 0.90 0.97 Good Fit RFI Above 0.90 0.95 Good Fit CN Above 200 139.61 Medium Fit RMR Standardized RMR ≤ 0,05 0.14 Low Fit GFI Above 0.90 0.77 Low Fit AGFI Above 0.90 0.74 Low Fit

*M = Model ; S = Saturated ; I = Independence

Absolute index of the goodness of fit of the models are shown by the RMSEA

index (0.064) with p-value (0.00), ECVI index (M*= 7.23, S*= 6.86, I*= 136.51), and

AIC index (M*= 1994.62, S*= 1892.00, I*= 37675.42). The index of RSMEA falls

below the critical value (≤ 0.08) and p ≤ 0.05 and therefore, it shows good fit. The value

of ECVI model shows minor difference with ECVI-saturated value than that with

ECVI-independence. Similarly, the index of AIC model shows minor differences with

AIC-saturated than that with AIC-independence. Thus, the indexes of ECVI and AIC

show that the model has a good fit. Meanwhile, the indexes of χ2 (1838.64), SRMR

(0.13), CAIC (M*= 2484.76, S*=6266.32, I*= 37874.25), GFI (0.77), and AGFI (0.74)

do not show a support to a good fit. These indexes indicate low model fit.

Incremental indexes of the goodnes of fit of the model that consist of NFI (0.95),

NNFI (0.97), CFI (0.97), IFI (0.97), and RFI (0.95) show that the model has good fit.

Other indicator, namely CN (141.85) indicates that the model has close fit. Overall

indexes show that most indexes indicate the good fit. It can be concluded that the new

model which integrates hedonic and utilitarian value has a good fit.

Model Comparison Tests

The comparison between hedonic and utilitarian values in their role to explain the user

satisfaction and intention repurchase were reviewed in terms of the fitness of both

model. The better fit suggests which model that has a better frame to explain the

dependent variable empirically. The comparison can be seen in detail in Appendix 5.

The absolute goodness-of-fit index of χ2 (Ut: 430.80; Hd: 852.42) indicates the

utilitarian value-based model has a higher goodness-of-fitm since it has the χ2 value that

is smaller than that of the hedonic value-based model. The RMSEA (Ut: 0,072; Hd:

0.075) and GFI (Ut: 0.87; Hd: 0.81) of both model do not have differences. The

RMSEA values fall below the critical value (0.08). The GFI values of both models are

close the critical value, above 0.90. Meanwhile, the SRMR value (Ut: 0.08; Hd: 0.07) of


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hedonic value-based model has better fitness because it is closer to the critical value (≤

0.05).

The incremental goodness-of-fit indexes yield the values of NFI (Ut: 0.96; Hd:

0.96), NNFI (Ut: 0.98; Hd: 0.97), CFI (Ut: 0.98; Hd: 0.98), IFI (Ut: 0.98; Hd: 0.98), and

RFI (Ut: 0.96; Hd: 0.95). Therefore, both models have no difference. Accordingly, it as

been proven that the hedonic and utilitarian value-based models have a comparable

level of model fit, which are a good fit. The indexes of NCP (Ut: 257.51; Hd: 533.45),

ECVI (Ut: 1.95; Hd: 3.63), AIC (Ut: 539.51; Hd: 1001.45), and CAIC (Ut: 775.34; Hd:

1288.14) indicate that the utilitarian value-based model has better fitness than the

hedonic value-based model, since the former one has the indexes values that are smaller

than the later one.

The measurement of goodness-of-fit shows that the utilitarian value-based

model has higher values of absolute goodness-of-fit indexes of χ2, NCP, ECVI, AIC,

and CAIC. Meanwhile, the hedonic value-based model is superior in the SRMR index.

Both models have good fit according to absolute goodness-of-fit indexes, such as

RMSEA and GFI, and incremental goodness-of-fit indexes, such as NNFI, CFI, IFI, and

RFI. In general, the two basic models have good fit. More specifically, the utilitarian

value-based model is better in its goodness-of-fit than the hedonic value-based model.

Sensitivity Test

The sensitivity test is carried out to determine the consistency of the results of this

study. In this research, the data are collected by means of electronic questionnaire and

paper questionnaire (manual). The differences in the result of this study may occur due

to the differences of the sampling technique. This study examined the research model

using 2 groups of the questionnaire data for the purpose of sensitivity test. Hypotheses

examinations are used to determine the consistency of the results of the study. The

results of the two samples analysis shows a consistency with the analysis of the overall

sample. The results of hypothesis testing using the two samples and overall sample are

presented in Table 7. The results of the analysis showed that from all hypotheses, only

the hypothesis H6 that show inconsistent result. Accordingly, the models used in this

study have consistent result.


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Table 7. Sensitivity Test

No. Hypothesis Coef t-stat

Status Online

Paper

Overall

Online

Paper

Overall

1. H1 Resem Flowex (ρx6x1)

0.80 0.61

0.76 6.36 5.96

9.29 Consistent

2 H2 Resem Satis (ρx8x1)

-0.04 0.05

0.003 -0.24 0.38

0.03 Consistent

3 H3 Resim Flowex (ρx6x2)

0.15 0.02

0.07 1.50 0.18

1.02 Consistent

4. H4 Flowex Satis (ρx8x6)

0.43 0.50

0.45 3.78 4.59

5.78 Consistent

5. H5 Infqu Perval (ρx7x3)

1.60 0.71

1.04 7.02 5.51

9.19 Consistent

6. H6 Sysqu Perval (ρx7x4)

0.34 0.77

0.73 0.90 4.78

5.18 Not

Consistent

7. H7 Servqu Perval (ρx7x5)

-0.60 -0.87

-0.87 -1.52 -5.32

-6.14 Consistent

8. H8 Perval Satis (ρx8x7) 0.59 0.84 0.75 6.04 7.36 9.62 Consistent

9. H9 Perval Inruse (ρyx7)

-0.07 -0.48

-0.31 -0.67 -3.51

-3.47 Consistent

10. H10

Satis Inruse (ρyx8) 1.16 1.05

1.06 5.89 6.52

8.82 Consistent

Discussion

The emotional responses were found to have significant positive effect on the flow

experience but insignificant positive effect on the user satisfaction. This can happen

because of the pleasant feeling experienced by the users when using online stores at

social media causes people is caused by affective response. Affective response that

experienced from social media environment makes people feel happy, excited, and

dominance in that environment. These feelings make people tend to perceive that they

get what they want, so it becomes exciting experience for him (flow experience). In the

end, this process will result in the sense of satisfaction. These facts are brand new

findings, and may complete previous research that has not considered the behavior of

the user satisfaction. Thus, this research provides a more comprehensive observations.

The imaginal responses are proved to have insignificant positive effect on the

flow experience. When people browse the online stores in social media, they are free to

be anybody, and to imagine anything about them and the products which are displayed

on the screen. This freedom does not lead the social media users in Indonesia to linger

in social media. This finding is inconsistent with previous study (Wang and Scheepers,

2012) which states that the imaginal response is an important antecedent of flow

experience. This can happen because, first, the search process in social media for

electronic shopping do not require the users to play special role. The users simply


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become themselves and look for the goods or services they want. Second, the users have

strong personality, so the experience with online store in the social media does not

trigger escapism and so does not help flow experience in using social media. Third, the

users are expected to have a good financial foundation and does not have any heavy

problems in real life. Thus, their tend to be reluctant to do escapism from daily life.

The flow experience has significant positive impact on the user satisfaction. This

result is consistent with Wu and Lu (2013), which reveals that perceived enjoyment as

hedonic characteristics is an important antecedent of the technology acceptance.

Pleasant experience when the users think that what they get is in accordance with his

wishes and lost in the imagination, will trigger a sense of satisfaction. The information

quality and system quality have significant positive effect on the perceived value. This

finding strengthens the study by DeLone’s & McLean’s (2003), Seddon’s (1997),

Wang’s (2007), and Wells’s et al. (2011). Sufficient information with easy accesses will

help online store to meet the productive needs of the users. When users get the

appropriate information with their desire, their hopes are fulfilled. This may also be

explained by the expectation disconfirmatory theory (Oliver, 1980). When user

expectations are confirmed in a certain online store, the users will feel that this online

store is more valuable than the others.

The service quality has a significant negative effect on the perceived value. The

examination on the different path produced unexpected finding, which is the path of the

service quality to the repurchase intention. The service quality has a significant positive

effect on the repurchase intention. It can be caused by several things. First, the

providers of social media-based online store have not established a good information

systems management. The research object in this study is retail stores in social media.

Second, problem of network or devices that are used by the users causes the users to

ignore the service. Third, the users focus more on the content, traceability, and ease of

purchase from the online store information systems. Fourth, the process of service

focuses on the time of interaction with service provider. Currently, service is seen as a

dynamic process between users and IT employees with participate each other (Shostack,

1987). Service is a type of interaction between user and IT employee in serving

functions, particularly in the form of interaction or transaction that involving the

exchange of core benefits, such as physical goods, valuable information, and other

valuable interactions.


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The perceived values are shown to have a significant positive impact on the user

satisfaction and a significant negative effect on the repurchase intention. Meanwhile, the

user satisfaction has a significant positive impact on the repurchase intention. This

finding is consistent with Wang’s (2007) research. The users perceived value is due to

the benefit from supply and monetary and non-monetary cost to gain the benefits

(Parasuraman and Grewal, 2000; Wang, 2007). The balance of both benefits will attract

the enthusiasm of individuals. Enthusiastic of interaction with information system

would lead to satisfaction.

The perceived values have a significant negative effect on the repurchase

intention. This suggests that satisfaction is the key to the business continuity.

Satisfaction is attached to the human cognitive. When a user wants to buy a product, he

will automatically remember the satisfaction level of his last experience with the

product, and remember where he bought it. The userswill return to that store. Thus, the

users satisfaction is a key mediator in the causal relationship between the perceived

values and repurchases intention.

In conclusion, on effort of online store going concern, the user satisfaction is the

primary antecedent. However, the user satisfaction will not stand without causes. On the

side of hedonic value, flow experience and emotional response have important role in to

create the users satisfaction. Moreover, the perceived values also have important role in

building the users satisfaction. The perceived values depend on the information quality

and systems quality. Whereas the flow experience depends on the imaginal responses.

However, unexpected results of these analysis have extended the findings of this study.

The service quality plays an important role in the repuchase intention.

Implications

A new model integrating hedonic and utilitarian values to explain the information

systems success in electronic commerce implies to the behavioral alignment in the

system analysis and design. The capability to supply the needs of hedonic and utilitarian

value on electronic trading system is an important thing to shapes the behavior of user

satisfaction and repurchase intention. The consequence is to include a balance of

hedonic and utilitarian composition within an electronic commerce information systems

will be maximize the reach of electronic commerce information systems adoption.

Online store design that sparked the imagination of users can be effective

strategic formulation for traders, such as demonstration of the use of clothing,


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testimonials, descriptions of comfort and quality of products/services, and so on. Thus,

the contents can trigger the imagination and sense of fun to the users. Imagination in

this context is required for electronic traders to minimize the expectated

disconfirmation. Furthermore, easy and friendly interaction with the online store page

can stimulate the users’ emotional responses. Several techniques like harmonious

colouring, good drawing arrangement, continuous update of the product in a balanced

rhythm, and words arrangement that can trigger fun tracking and shopping to the users.

Comfortable and pleasant interaction will generate user solubility. Then the solubility

will trigger the users satisfaction and puchase intention.

The information quality and system quality are the keys to perceived value. As a

result, online store owners should concentrate on brief and complete information

presentation. A simple website design with interesting colour composition can maintain

the stability of system information performance. The service quality has an important

role on the repurchase intention, so the online store owner must maintain the

comfortable interaction of the user with online store information system. The

comfortable interaction will generate the perceived good service in the users cognitive.

Satisfaction is a key to connect the perceived value and flow experience to the

repurchase intention. This requires the online store owners to maintain user satisfaction

as the spearhead to achieve business sustainability (going concern). The users

satisfaction can be monitored by conducting a survey shortly after the customers do

online shopping. In fact, the testimony could be used as input for continous information

system improvement. Finally, the integration of the hedonic and utilitarian values in

accordance with the users’ needs can widen the coverage of electronic commerce

information systems. Emphasis on certain indicators of hedonic and utilitarian should be

considered according to the research results and the performed business model.

Conclusion

This study aims to build a new model that integrates hedonic and utilitarian values in

explaining the behaviour of the users satisfaction and repurchase intentions in electronic

trading on social media. A survey method is employed to obtain primary data using

questionnaires. Measurement scale in the questionnaire was adopted from the study by

Wang (2007) and Wang and Scheepers (2012). The unit of analysis in this study is the

individual. Such individuals are customers who shop online via social media. The

respondents of this study comprise of the online shop consumers on Yogyakarta and


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Medan, Indonesia. The sample selection procedure in this study is a snowball method.

A total of 227 questionnaires are collected for hypothesis testing. Seven of ten

hypotheses are supported. Based on these results, we can conclude as follows.

The hedonic value does not entirely have positive and significant effect on the

flow experience in electronic shopping via social media. The result shows that the

emotional responses have a positive and significant impact on the flow experience, but a

positive and insignificant impact on the users satisfaction. Meanwhile, the imaginal

response has a positive and insignificant effect on the flow experience. The flow

experience has a positive and significant impact on the users satisfaction in electronic

shopping via social media.

The utilitarian value does not entirely have positive effect on the perceived

values in electronic shopping via social media. The result indicates that the information

quality and system quality have a positive and significant effect on the perceived values.

However, service quality has a negative effect on the perceived values. The perceived

values have positive and significant impact on the user satisfaction in electronic

shopping via social media. The users satisfaction have a positive and significant impact

on the users repurchase intention.

The hedonic value-based model and utilitarian value-based model have a good

fit predicate. The utilitarian value-based model has higher goodness-of-fit on the χ2

index (absolute model fit). Te hedonic value-based model has a higher model fit on the

SRMR index. On other absolute and incremental indexes of model fit, both models do

not have different levels of model fit. In some other indexes such as NCP, ECVI, and

AIC, the utilitarian value-based model has a better model fit. Thus, it can be concluded

that the utilitarian value-based model has a better model fit than the hedonic value-

based model.

Contributions

Humans have a hedonic needs that are stimulated by the right brain and utilitarian needs

that are stimulated by the left brain (Hirschman and Holbrook, 1982). The integration of

hedonic characteristics in information systems success model gives a new colour in the

world of information systems science. In line with Hirschman and Holbrook statement

(1982), the hedonic characteristics included in the model of information system success

is not to replace the old models, but to widen the information system adoption.


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Harmonization in the values of art and functionality of information system will

wake up the balance of information system. The balance refers to the availability of

both benefit and enjoyment values in information system. This study contributes to the

e-commerce website designers and electronic traders to planned their online store

website. Electronic commerce information system manager can insert the content of

hedonic and utilitarian in their design of online store page. It is an effort to reach a

wider market adoption and achieve business sustainability (going concern). The

emphasis on specific content should be determined by the business model, experience

and ongoing research by electronic commerce information systems manager.

Results of this study can be taken as consideration in order to achieve efficacy

and efficiency in the use of social media. Social media can be used as an electronic

trading platform. Creative and productive users can design a website and social media

pages as an effort to get consumers from other users. Thus, this study can stimulate

social media users to be able to take productive action. As we know, entrepreneurship is

an important discourse of Indonesian government in an effort to curb unemployment

and poverty.

Limitations

New model that integrates the hedonic and utilitarian values in this study have several

limitations. That limitations may reduce the meaning of the results (Sumiyana, 2007).

First, the samples were selected from social media users in Yogyakarta and Medan. The

users of information technology system in these regions have already familiar with

social media and online shopping activities. This condition causes the dimensions of

imaginal response, emotional response, information quality, system quality, service

quality, has been embedded into the individual cognitive long enough.

Second, the individual values associated with behavioral intention is

dynamically evolving over time. Research with construct model like this is not capable

to cacth a dynamic behavioral development of individuals, because individuals always

move to the creation, sharing of resources and system update (Sumiyana, 2007e;

2007b).

Third, this study uses the online store at social media as a research object.

Therefore, the generalization of this study is limited to the retail traders in social media.

Fourth, this study uses a number of measurement items based on the respondents’

perception that could bring potential bias because it only measures the constructs based


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on respondents viewpoint. Fifth, the model developed in this study is relatively new;

therefore we need further validation for external and internal validity.

Suggestions for Further Research

Previous research or similar research with this study use the survey method. Internal

validation needs to be done to strengthen the existing research results. Therefore,

similar studies using experimental method will increase the internal validity of the

model. Additionally, further research with different research objects, especially on good

business platform, will help generalize the model.

The further study is also expected to identify the characteristics of different

samples. For example, the numbers of a consumer conducting electronic shopping are

identified, or the users may also be distinguished as ever performing online shopping

that have no and have never did. It is useful for robustness test, in order to know the

power of model when the assumptions are revoked. The development of models with

new variables and more sophisticated research design will add to the wealth of existing

knowledge and to answer the actual problem.


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Appendix 1 Constructs Measurement

Latent Predictors

Imaginal Response

RESIM1: Playing social media helps me temporarily esape from the world of reality RESIM2: Playing social media helps me temporarily esape from problem and pressures RESIM3: Playing social media helps me temporarily esape from things unpleasant and worrisome RESIM4: Playing social media enables me to project myself into a particular role RESIM5: Playing social media enables me to project myself into a particular character RESIM6: Playing social media enables me to project myself into a particular task.

Emotional Response

RESEM1: To what extent do you feel happy or unhappy when playing social media? RESEM2: To what extent do you feel pleased or annoyed when playing social media? RESEM3: To what extent do you feel satisfied or unsatisfied when playing social media? RESEM4: To what extent do you feel relaxed or bored when playing social media? RESEM5: To what extent do you feel stimulated or relaxed when playing social media? RESEM6: To what extent do you feel exited or calm when playing social media? RESEM7: To what extent do you feel in control or cared for when playing social media? RESEM8: To what extent do you feel controlling or controlled when playing social media? RESEM9: To what extent do you feel dominant or submissive when playing social media?

Flow Experience

FLOWEX1: I have fun when I am playing social media. FLOWEX2: Playing social media provides me with a lot of enjoyment. FLOWEX3: I enjoy playing social media. FLOWEX4: When playing the social media, my attention is focused on the social media. FLOWEX5: When playing the social media, I am absorbed intensely in the social media. FLOWEX6: When playing the social media, I concentrate fully on the social media


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FLOWEX7: Time appears to go by very quickly when playing the social media. FLOWEX8: Sometimes I lose track of time when playing the social media. FLOWEX9: Time flies when playing the social media.

Information Quality

INFQU1: The e-commerce system provide the precise information you need INFQU2: The information content meets you need INFQU3: You feel the output is reliable INFQU4: The e-commerce system provide up-to-date information

System SYSQU1: The e-commerce system is user friendly SYSQU2: The e-commerce system is easy to use SYSQU3: When you have problem, the e-commerce system service show a sincere interest in solving it. SYSQU4: The e-commerce system service is always willing to help you SYSQU5: You feel safe in your transaction with the e-commerce system service in terms of security and privacy protection

Service Quality

SERQU1: The e-commerce system service has the knowledge to answer your questions SERQU2: The e-commerce system service give you individual attention SERQU3: The e-commerce system service understand your specific needs

Peceived Value

PERVAL1: The product/service of the e-commerce system is a good value for money PERVAL2: The price of the product/service of the e-commerce system is acceptable PERVAL3: The product/service of the e-commerce system is considered to be a good buy

Satisfation SATIS1: You are satisfied with thw e-commerce system SATIS2: The e-commerce system is high quality SATIS3: The e-commerce system has met you expectations


INREUSE1: Assuming that you have access to the e-commerce system, you intend to reuse it. INREUSE2: You will reuse the e-commerce system in the future. INREUSE3: You will frequently use the e-commerce system in the future.


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Appendix 2 Convergent Validity Test

Observed

variable

Factor

Loading AVE

C. Alpha

Eliminated

Item

Cronbach

Alpha

Construct

Reliabilitas

RESIM1 0,64

0,565

0,739

0,787 0,816

RESIM2 0,61 0,749

RESIM3 0,00 0,864

RESIM4 0,89 0,687

RESIM5 0,85 0,702

RESIM6 0,73 0,732

RESEM1 0,82

0,674

0,894

0,91 0,942

RESEM2 0,84 0,893

RESEM3 -0,05 0,942

RESEM4 0,86 0,892

RESEM5 0,82 0,895

RESEM6 0,91 0,888

RESEM7 0,77 0,896

RESEM8 0,8 0,893

RESEM9 0,74 0,897

FLOWEX1 0,85

0,695

0,947

0,954 0,929

FLOWEX2 0,85 0,947

FLOWEX3 0,83 0,948

FLOWEX4 0,83 0,948

FLOWEX5 0,87 0,946

FLOWEX6 0,85 0,948

FLOWEX7 0,84 0,948

FLOWEX8 0,81 0,949

FLOWEX9 0,77 0,951

INFQU1 0,88

0,74

0,894

0,921 0,921 INFQU2 0,89 0,891

INFQU3 0,83 0,904

INFQU4 0,84 0,901

SYSQU1 0,72 0,594 0,865 0,882 0,883


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SYSQU2 0,74 0,86

SYSQU3 0,81 0,847

SYSQU4 0,78 0,858

SYSQU5 0,8 0,854

SERQU1 0,89

0,701

0,796

0,877 0,879 SERQU2 0,81 0,836

SERQU3 0,81 0,845

PERVAL1 0,91

0,852

0,928

0,945 0,867 PERVAL2 0,94 0,912

PERVAL3 0,92 0,917

SATIS1 0,87

0,798

0,927

0,936 0,777 SATIS2 0,91 0,894

SATIS3 0,9 0,898

INRUSE1 0,91

0,792

0,892

0,927 0,764 INRUSE2 0,88 0,89

INRUSE3 0,88 0,901


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Appendix 3 Communality Factor Values

FLOWEX PERVAL SATIS INRUSE RESEM RESIM INFQU SYSQU SERQUFLOWEX1 0,85 FLOWEX2 0,85 FLOWEX3 0,83 FLOWEX4 0,83 FLOWEX5 0,87 FLOWEX6 0,85 FLOWEX7 0,84 FLOWEX8 0,81 FLOWEX9 0,77 PERVAL1 0,91 PERVAL2 0,94 PERVAL3 0,92 SATIS1 0,87 SATIS2 0,91 SATIS3 0,9 INRUSE1 0,91 INRUSE2 0,88 INRUSE3 0,88 RESIM2 0,61 RESIM4 0,89 RESIM5 0,85 RESIM6 0,73 RESEM1 0,82 RESEM2 0,84 RESEM4 0,86 RESEM5 0,82 RESEM6 0,91 RESEM7 0,77 RESEM8 0,8 RESEM9 0,74 INFQU1 0,88 INFQU2 0,89 INFQU3 0,83 INFQU4 0,84 SYSQU1 0,72 SYSQU2 0,74 SYSQU3 0,81 SYSQU4 0,78 SYSQU5 0,8 SERQU1 0.89 SERQU2 0.81 SERQU3 0,81


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Appendix 4 AVE Results

FE PV St RI ER IR IQ SQ SeQ

FE 0,69

PV 0,04 0,85

St 0,22 0,56 0,79

RI 0,18 0,25 0,66 0,79

ER 0,36 0,12 0,18 0,12 0,56

IR 0,00 0,02 0,01 0,01 0,00 0,67

IQ 0,04 0,45 0,28 0,13 0,11 0,06 0,74

SQ 0,00 0,12 0,06 0,03 0,00 0,08 0,17 0,59

SeQ 0,01 0,00 0,00 0,00 0,03 0,09 0,10 0,49 0,69


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Appendix 5 Models Comparison

GOF

index Indicators

Etimation Best

Hedonis Utilitarian

Chi-Square Small score

(p>0.05)

852,42(p=0.00) 430,80 (p=0.00) Utilitarian

NCP,

Interval

Small score,

narrow interval

533,45

(449,87;624,70)

257,51 (200,00;

322,73)

Utilitarian

RMSEA,

p(close-fit)

RMSEA ≤ 0,08

(p≥0.50)

0.075 (p=0.00) 0.072 (p=0.00) Equal

ECVI Small score, close

with ECVI

saturated

M*=3,63,

S*=2,94, I*=75,56

M*=1,95, S*=1,67,

I*=44,68

Utilitarian

AIC Small score, close

with AIC saturated

M*=1001,45,

S*=2689,35,

I*=20984,09

M*=539,51,

S*=462,00,

I*=12330,74

Utilitarian

CAIC Small score, close

with CAIC

saturated

M*=1288,14,

S*=2689,35,

I*=20984.09

M*=775,34,

S*=1530,15,

I*=12427,85

Utilitarian

NFI Above 0.90 0.96 0.96 Equal

NNFI Above 0.90 0.97 0.98 Equal

CFI Above 0.90 0.98 0.98 Equal

IFI Above 0.90 0.98 0.98 Equal

RFI Above 0.90 0.95 0.96 Equal

CN Above 200 133.09 146.47 Equal

RMR Standardized RMR

≤ 0,05

0.07 0,08 Hedonis

GFI Above 0.90 0.81 0,87 Equal

AGFI Above 0.90 0.78 0,83 Utilitarian


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