A Study on the Influence of Digital Experience and Purchase in the 4^th Industrial Revolution : Focusing on Differences between Satisfied, Neutral, and Dissatisfied Groups

A Study on the Influence of Digital Experience and Purchase in the 4

Industrial Revolution : Focusing on Differences between

Satisfied, Neutral, and Dissatisfied Groups

Sang Hee Jung*․Sang-Jik Lee**

Abstract

One of the most considerate phenomena of the era of the Fourth Industrial Revolution is the use of digital devices. Digitalization is rapidly advancing through all areas of industry and life. Customer journey with digitalization is looking totally different from previous customer journey. The research targets were users of fashion, automobiles, cosmetics and online shopping malls. We analyzed 300 people for each valid questionnaire. The results of the study are as follows. First, it has been proven that digital experience affects positive (+) impact on purchasing intention and positive (+) impact on recommending intention and negative impact (-) on switching intent and subsequently affects positive impact (+) to purchase and incase of switching intent, negative impact (-) to purchase. Unlike traditional methods such as SPC(Service Profit Chain), the Digital experience to Purchase process Chain (DPC) has been identified to be suitable in the digital age. Second, the digital satisfied group (5 score-very satisfaction) has shown same result as above. However the digital neutral group (even though 4 score- satisfaction in five-point scale), specially in a highly competitive industry, has different from the satisfied group and 3 score-normal is same as dissatisfied group. It means that this group is that If there is a high level of attractiveness of substitute goods, there is a high possibility of switching them. It has supported Jones and Sasser [1995]

that there have been two types of loyalty of true long-term loyalty and what we call false loyalty in the highly competitive industry zone which is commoditization or low differentiation, many substitutes, low cost of switching. Identifying true loyalty and false loyalty is crucial to establishing a customer experience strategy. it is necessary to actively utilize long-term digital experiences strategy to increase the total satisfaction of digital experience through all of customer purchasing journey in order to enhance the digital customer experience. It is difficult to see the effect as a one-time event. It should be scaled over the entire customer purchase process over a long period of time, which can positively affect purchase intention, recommendation intention, and conversion intention. This is also why it is difficult for second-runners to overtake first-runners in a short period.

Keywords：Fourth Industrial Revolution, Customer Experience, Digital Experience, Satisfaction Group in Digital Customer Experience

1)

Received：2019. 07. 09. Revised : 2019. 08. 21. Final Acceptance：2019. 08. 30.

** First Author, Ph.D. Candidate, Graduate School of Venture, Hoseo University, e-mail：[email protected]

** Corresponding Author, Professor, Graduate School of Venture, Hoseo University, 2497, Nambusunhwan-ro, Seocho-gu, Seoul, 06724, Korea, Tel：+82-2-2059-2313, e-mail：[email protected]

1. Research Purpose

CNBC (Consumer News and Business Chan- nel)[2018] reported that more than a dozen retailers including major department store chains, mattress sellers and shoe companies filed for bankruptcy protection in 2018, des- pite strong consumer spending that other- wise lifted the U.S. economy. The biggest bankruptcy of 2018 was Sears, a 125-year-old business that was once the largest retailer in the U.S. Forrester Consulting recently sur- veyed 1269 companies in the Asia Pacific region on their experience business. And ac- cording to the Business Impact of Customer Experience report, companies investing in cus- tomer experience are much higher than com- panies that do not invest 23 percent in sales growth (13 percent).

The report analyzed that a customer-ex- perience-investing firm spends more than a general-purpose firm but spends twice as much in the long run. Jean-Piper Gartner vice pre- sident and chief analyst, We need to invest in the digital customer experience to enhance the customer experience across customer buy- ing behavior. Digital customer experience is a collective term for customer’s emotions, reac- tions, and behaviors in the process of com- munication and trading with companies using smart devices that customers have [SAP, 2017; Hoffman and Novak, 2009; Rose et al., 2012]. But so far there is a limit to the main study of customer experience which has been to assess service quality and to analyze the impact of this quality on customer behavior through typical model of the SERVQUAL (a multi-dimensional research instrument, desi- gned to capture consumer expectations and perceptions of a service along the five dimen- sions that are believed to represent service quality) model introduced by Zeithaml et al.

[1996]. However, Verhoef et al. [2009] has stated that focusing on this is very important because customer experience can have a com- petitive advantage, and customer experience should be more than assessing service qua- lity. The biggest difference between a cus- tomer experience and a service quality is that the customer experience includes customer emotion. The quality of service has been fo- cused on cognitive appraisal and the emo- tional role of the customer has been ne- glected.

Pine II and Gilmore [1998] stated that services is intangible, and experiences are memorable in experience economy. The big difference between service and experience is that the service disappears over time, but the experience is long in Harvard business review.

They have seen the customer experience as a holistic process built on the customer jour- ney. This study also analyzes the impact of digital customer experience through a series of processes ranging from digital customer experience to brand loyalty to purchase, so it is a Service Profit Chain model that first theorizes this series of processes [Heskett et al., 1994].

With the advent of this era of the Fourth

Industrial Revolution, digitalization is rapi-

dly advancing through all areas of industry

and life and customer journey with digitali-

zation is looking totally different from pre-

vious customer journey [David, 2010]. The

purpose of this study is to investigate the

Digital experience to Purchase process chain

suitable for the digital age unlike traditional

methods such as SPC (Service Profit Chain)

and the difference between digital experience

satisfaction group, neutral group and dissa-

tisfied group on the influence of digital cus-

tomer experience on brand loyalty and pur-

<Figure 1> Service Profit Chain (SPC) Model

chase. Jones and Sasser, Jr. [1995] stated

that the only truly loyal customers are very satisfied customers only, not satisfied cus- tomers in highly competitive industry due to commoditization or low differentiation or many substitutes or low cost of switching.

There have been three main characteri- stics of previous studies on digital customer experience. First, research was limited to specific industries or product groups [Kawaf and Tagg, 2017]. Second, the attributes that affect the customer’s journey have also been limited to specific attributes [Shobeiri et al., 2014]. Third, devices that affect the digital experience are often limited to specific de- vices such as the Internet or mobile devices [Daurer et al., 2015]. Chung and Jung [2018]

studied the relationship between digital expe- rience and purchase, derived digital experience attributes comprehensively and systemati- cally from various devices and applied them to four product groups. In this study, the research was conducted based on the basis of this study. In addition, we applied this to three product groups and compared the di- fferences between digital customer experience satisfaction group and neutral group and dissatisfied group.

This research is very timely in the current research situation where the history of re- search on the digital customer experience is short and there are no concordant results on the digital experience attributes and satisfac- tion and dissatisfaction. The purpose of this study is as follows.

This study was very timely in the current

research situation where the history of re- search on digital experience is short and there are no agreed results on digital experience attributes.

First, this study has analyzed the rela- tionship between digital experience, recom- mending intention, purchasing intention and switching intention for four products of fa- shion, auto, cosmetics and online shopping malls in customer journey.

Second, this study has found out three di- fferent digital experience group of satisfied, neutral and dissatisfied group and the analy- sis of differences in effect on recommending intention, purchasing intention, switching in- tention and finally purchase.

Third, based on this results, theoretical and practical implications are suggested.

2. Theoretical Background

2.1 SPC(Service Profit Chain) Model

Since this study analyzes the impact of digital experience through a series of pro- cesses ranging from digital experience to brand loyalty to purchase, we will first look at the SPC (Service Profit Chain model) that theorizes this series of processes.

Heskett et al. [1994] defined the relation- ship between service quality (SQ), customer satisfaction (CS), customer loyalty (CL) and firm’s financial performance (Profitability) as as a Service Profit Chain (SPC).

The concept of SPC implies that higher

service quality increases customer satisfac-

tion, customer satisfaction increases custo- mer loyalty, and ultimately loyalty increases corporate profits. Since then, many scholars have been studying various relationships between components [Mittal and Kamakura, 2001; Yee, Yeung, and Cheng, 2011; Kasiria et al., 2017; Chuah et al., 2017]. Service quality is focused on the perceived service quality that consumers perceive and a typi- cal example is the SERVQUAL model pre- sented by Zeithaml et al. [1990]. It consists of five elements : Tangibles at organizational level, Reliability element and Responsive- ness at individual level, Assurance, Empathy factor. On the other hand, Grönroos [1984]

divides quality into technical quality and functional quality. Technical quality is the quality related to the final product received by the consumer. Functional quality is the quality related to the process in which the service is provided. Quality is usually per- ceived to be good or high when the actual exceeds expectation due to the difference bet- ween what is expected to be measured using the GAP model and the actual one. Pine II and Gilmore [1998] stated that an experience occurs when a company intentionally uses services as the stage, and goods as props, to engage individual customers in a way that creates a memorable event. Services is intan- gible, and experiences are memorable in expe- rience economy. The big difference between service and experience is that the service disappears over time, but the experience is long, As goods and services become commo- ditized, the customer experiences that com- panies create will matter most in Harvard business review. So the relationship between Digital Experience (DX), customer satisfac- tion (CS), customer loyalty (CL) and firm’s financial performance (Purchase) as as a the Digital experience to Purchase process Chain

(DPC) should be studied in the digital age.

Oliver [1997] defined satisfaction as the result of post-use evaluation, including both cognitive and emotional factors (containing both cognitive and affective elements). Accor- ding to the expectation-inconsistency theory, the customer evaluates satisfaction by com- paring perceived results with previous expec- tations. There are two dimensions to this customer satisfaction. In other words, it is a transaction-specific satisfaction and a cumu- lative overall satisfaction. Specific transac- tion unit satisfaction is a customer’s assess- ment in connection with a particular commo- dity transaction, episode, or service, and cu- mulative overall satisfaction is an overall assessment of the services or products pro- vided so far [Oliver, 1997].

Loyalty is the intrinsic commitment of a customer who repeatedly buys a product or service that is consistently preferred despite any circumstance or competitor attraction [Oliver, 1999]. Dick and Basu [1994] have argued that true loyalty occurs when there is a high relative attitude and repeat pa- tronage. Customer loyalty is largely divided into attitudinal loyalty and behavioral lo- yalty [Dick and Basu, 1994; Olive, 1999]. In the early days, loyalty was focused on a sin- gle dimension component, ie, behavioral aspect [Bodet, 2008], but it was also considered an attitudinal aspect. Attitudinal loyalty is seen to include both cognitive, affective, and cona- tive factors [Oliver, 1999]. Most scholars now agree that loyalty consists of multidimen- sional elements that take both behavior and attitude [Velazquez et al., 2011].

Although attitude loyalty and behavioral

loyalty are related, many studies have been

conducted as separate concepts. Behavioral

loyalty focuses on the consequences of lo-

yalty such as repurchase, and attitudinal

loyalty focuses on the cognitive base. Beha- vioral loyalty measurement variables for mea- suring loyalty are repurchase probability, spe- cific brand long-term selection probability, and brand switching behavior. Attitudinal loyalty measures include word of mouth in- tention, resistance to competitive alterna- tives, intention to repurchase, and intention to pay premium price. Many studies have used repurchase intention, word of mouth, and willingness to pay as loyalty measures.

[Velazquez et al., 2011].

Customer loyalty and future accounting performance generally have a positive corre- lation, but most are nonlinear. And the type of nonlinearity is decreasing returns [Anderson and Mittal, 2000]. Customer loyalty has been shown to translate customer satisfaction into corporate profits, and customer satisfaction has been found to increase future cash flow and ultimately to increase shareholder value [Gruca and Rego, 2005]. Of course, corporate profits are not made only through customer loyalty. As customer satisfaction increases, customer complaint handling costs are re- duced, recommendation can reduce new cus- tomer acquisition costs, and price elasticity can be reduced.

2.2 Digital Experience

Digital customer experience means cus- tomer’s emotions, reactions and behaviors that occur in the process of online commu- nication (eg, search, question, review, evalua- tion, change of personal information) or trading (eg, purchase and payment, return, charge and gift, open a bank account, transfer, etc.) with companies using digital devices (eg, smart- phones, tablets, PCs, etc.) that customers have. In other words, digital customer expe- rience is a collective term for customer’s emo-

tions, reactions, and behaviors in the process of communication and trading using smart devices [SAP, 2017].

The Fourth Industrial Revolution can be summed up as a combination of the Cyber and Physical System worlds (CPS) [Akeson, 2016]. With the advent of this era of the Fourth Industrial Revolution, digitalization is rapidly advancing through all areas of industry and life. Customer journey with di- gitalization is looking totally different from previous customer journey [David, 2010]. A variety of factors are affecting this customer journey, one of which is digital customer ex- perience, and its importance is increasing [Hoffman and Novak, 2009]. Yaffe et al.

[2019] has reported that as the forces of the Fourth Industrial Revolution accelerate, consumers are enjoying the benefits of rapid innovation and new models of consumption in digital age, but also struggling to main- tain a sense of connection and understanding our rapidly changing world and also physical retail is dead from a threat of the impact of digital disruption in an editorial of these titles “the experience economy is booming, but it must benefit everyone” World Econo- mic Forum.

However until recently, research on cus- tomer experience has been to assess service quality and to analyze the impact of this qua- lity on customer behavior. The representa- tive model is the SERVQUAL model [Zeithaml et al.,1996]. However, Verhoef et al. [2009]

stated that focusing on this is very important

because customer experience can have a com-

petitive advantage, and customer experience

should be more than assessing service qua-

lity. The biggest difference between a custo-

mer experience and a service quality is that

the customer experience includes customer

emotion. The quality of service has been fo-

cused on cognitive appraisal and the emo- tional role of the customer has been neglec- ted. They see the customer experience as a holistic process built on the customer jour- ney. It is because the customer always expe- riences, whether the person is normal or bad.

In this study, we focus on experiential fac- tors based on hedonic values ​ ​ [Schmitt, 1999].

However, Vargo and Lusch [2006] found that customer experience is the most important part of practical activity. Therefore, both he- donic and practical aspects should be consi- dered. Like the definition of SAP [2017] above, the theoretical foundation of the customer experience lies in that the customer expe- rience is achieved by a combination of touch- points with all the clues to be met by the customer.

According to the literature review, Holbrook and Hirschman [1982] are the first to ap- proach consumption behavior as a customer experience while finding meaning in consump- tion experience. Pine and Gilmore [1999] con- clude that the retail experience is composed of a total area of ​ ​ aesthetic, amusing, edu- cational, and escapist, and that various fac- tors have a static or dynamic flow within the experienceable environment. In this part, the concept of customer experience came out.

Understanding customer perceptions in the digital environment and evaluating overall service quality have focused on digital cus- tomer behavior research.

Many studies still use E-SERVQUAL to focus on customer perception of service qua- lity. Gradually, the focus of research has been shifted from offline to online. Hoffman and Novak [2009] viewed the digital customer experience from a cognitive perspective that interacts digitally. However, Rose et al. [2012]

emphasized the importance of customer emo-

tions in digital experience. Martin et al. [2015]

looked at the customer experience from a cognitive and emotional perspective. Never- theless, we are still in the early stages of understanding customer behaviors in a digi- tal environment [Trevinal and Stenger, 2014].

There is a lack of clear understanding or de- finition of digital experience variables [Martin et al., 2015].

2.3 Digital Experience Attributes

Schmitt [1999] suggests that there are two factors that characterize the specific frame- work for experiential marketing. One is the Strategic Experience Modules (SEM), which states that there are many types of expe- rience types. The other is ExPros (Experi- ence Producers), which are the various enti- ties that convey this experience. The creation of five different types of experience is called experiential marketing, and SEM suggests think, feel, act, sense, and relate. Fornerino et al. [2006] divided the customer experience into five dimensions : sensorial-perceptual, affective, physical-behavioral, social, and cog- nitive. Brakus et al. [2009] empirically tested four dimensions of sensory, intellectual, affec- tive, and behavioral. Brakus et al. [2009] sug- gested that these experiential elements ulti- mately create an overall experience. Klaus [2013] stated that customer experience is context specific, so that variables that affect the context will be different, or different levels will exist, even if they are the same variable.

In this study, we analyze the major literature

related to digital customer experience from

2013 to 2018, and derive attributes of cus-

tomer’s digital experience. The results are

summarized in <Table 1> and <Table 2>.

Group Characters sources Satisfaction Typically believes that the company excels in understanding and addressing his or her personal

preferences, values, needs, or problems. To figure out how to satisfy customers in this fashion, a company has to excel at listening to customers and interpreting what they are saying.

Jones Sasserand [1995]

Neutral

Satisfied with the underlying product or service, but want to provide consistent support. And to ensure that a neutral and satisfying customer does not go back to the dissatisfied area in the event of bad luck, companies need a responsive recovery process. A well-designed support service makes basic products or services easy to use.

Dissatisfaction

Will have a problem with the core value of the company’s product or service. A fundamental element that everyone in the industry can expect from a customer. The basic products that customers want are the competitors’ improvements and the arrival of new competitors and new technology redefines the game.

<Table 3> Three Group of Satisfaction and Characters

2.4 Digital Experience Satisfaction, Neutral

and Dissatisfied Group

Jones and Sasser [1995] stated that cus- tomer satisfaction, neutral and dissatisfac- tion information can be a critical barometer of how well a company is serving its customers.

This information can show a company what it needs to do to increase its customer satisfac- tion level by level until the majority of its customers are totally satisfied. The three- phase approach to increase customer satisfac- tion has important implications. First, different actions are required to raise the satisfaction of customers of a family of products or services whose level of satisfaction differs. Second, it is absolutely critical to accomplish the three stages in order. It is possible to make a quantum leap-to move customers from neutral to comple- tely satisfied, for instance-by completely rede- signing the product or service, by introducing new technology, or by reengineering the under- lying delivery process.

Jones and Sasser, Jr. [1995] stated that the only truly loyal customers are very satisfied customers in highly competitive industry due to commoditization or low differentiation or many substitutes or low cost of switching.

The customer’s overall satisfaction was divided into three groups, from the highly dissatis- fied to the highly satisfied, five-point scale.

That is, grouping was done from 1 point (very

unsatisfaction) to 3 point (normal) as dissa- tisfied group, 4 point (satisfaction) as neu- tral group and 5 point (very satisfaction) as very satisfied group as shown in <Table 4>.

Loyalty Group Answer

Score Answer Description Satisfied Group 5 Very Satisfaction

Neutral Group 4 Satisfaction

Dissatisfied

Group 3, 2, 1 Normal, Unsatisfaction,Very Unsatisfaction

<Table 4> Loyalty Group in Highly Competitive Industry

When applying the definition of Oliver [1997] to customers in the digital world, there are two ways to measure digital customer satisfaction. One is digital transaction-spe- cific satisfaction. The other is digital cumu- lative overall satisfaction. Digital transaction- specific satisfaction is the evaluation of a customer in connection with a particular com- modity transaction, episode, or service through digital. In this study, the digital experience as one of transaction-specific satisfaction is measured by digital functional factors, digi- tal quality factors and digital personalization service factors based on 12 digital experience attributes studied by Chung and Jung [2018].

Another is digital cumulative satisfaction.

Overall digital satisfaction is the total digi-

tal experience evaluation of the services or

products provided so far. The purpose of this

study is to analyze the digital cumulative

<Figure 2> Research Model

overall satisfaction group divided into digital

satisfied, neutral, and dissatisfied groups.

When we apply Oliver [1997] expectation-in- consistency theory to the digital experience world, we can analyze whether the customers have been able to compare the perceived di- gital transaction-specific satisfaction with the previous digital experience expectations of the digital-cumulative overall experiences.

Positive digital customer experience resulted in satisfaction, reliability, re-inquiry, repur- chase intention, and loyalty [Verhoef et al., 2009]. Digital customer experience is an oppor- tunity for companies to increase their involve- ment and is a key to long-term customer re- lationship development [Wirtz et al., 2013].

Rose et al. [2011] conclude that purchasing intentions are the best element of digital cus- tomer experience outcomes. Rose et al. [2012]

confirmed this again through empirical analy- sis. Luo et al. [2011] show that digital cus- tomer experience improves repurchase inten- tions and recommending intentions in online games through empirical analysis. Brodie et al.

[2013] found that digital customer experience has an impact on continued intent to use.

3. Research Model and Hypothesis

3.1 Research Model

This study has focused on investigation of

the digital experience to purchase process chain and the role of the satisfied, neutral and dis- satisfied group in the relationship between digi- tal customer experience, purchase intention, recommending intention and switching inten- tion. Therefore, in order to investigate the role of the satisfied, neutral and dissatisfied group in a series of processes leading to digital expe- rience, purchase intention, recommending inten- tion and switching intention based on previous research. Until now, studies have been limited to specific areas (e.g. industry- specific studies, specific digital characteristics, and device-spe- cific studies). However Chung and Jung [2018]

studied the relationship between digital expe- rience and purchase, derived digital experience attributes comprehensively and systematically through various devices, and applied them to four product groups. Therefore functional fac- tors, quality factors and personalization service factors of digital experience were used as they were in Chung and Jung (2018). Unlike many previous studies, which use purchase intention as a final dependent variable, this study have used the purchase amount and purchase ex- penditure cost as final dependent variables.

In addition, this study has analyzed the diffe-

rence between the three groups of satisfied,

neutral and dissatisfied group in the relation-

ship between digital experience and purchasing

intention, recommending intention, switching

intention and sequentially purchase.

3.2 Hypothesis

Based on these previous studies, the hypo- thesis of this study is set as follows. When customers have a positive digital experience, they are satisfied with satisfaction, reliabi- lity, inquiry, purchasing intention, repurchase intention, and loyalty [Verhoef et al., 2009].

Luo et al. [2011] show that digital experience improves the recommending intentions in on- line games through empirical analysis. In this study, the following hypothesis was establi- shed in relation to digital experience and recommending intention.

H1. The digital experience will have a posi- tive (+) effect on the recommending in- tention

Rose et al. [2011] concluded that dual pur- chasing intent was the best component of the digital customer experience and later verified again by empirical analysis [Rose et al., 2012].

Brodie et al. [2013] stated that digital expe- riences affect the intention of continuous use.

H2. The digital experience will have a posi- tive (+) impact on purchase intention Experiential satisfaction is influenced by affective experience, novelty-seeking, expe- riential quality, and experiential equity while experiential satisfaction and experiential equity influence switching intention [Wu and Cheng, 2018].

H3. The digital experience will have a nega- tive (-) impact on switching intention.

Therefore, the digital experience is an op- portunity to increase the involvement of the customer in the brand, and it can be the key to develop the customer relationship in the long term [Wirtz et al., 2013].

H4. The recommending intention will have a positive (+) effect on the purchase.

In addition, a study by Chung and Jung [2018b] also stated that the intent to use and the willingness to recommend a person has a posi- tive influence on the purchase. In this study, the following hypotheses were established : H5. The purchasing intention will have a posi-

tive (+) effect on the purchase.

Meanwhile, according to Liang et al. [2018], who studied Airbnb’s customer satisfaction, intent to switch, and its relationship with purchases, there was a negative impact.

H6. The switching intention will have a nega- tive (-) effect on the purchase.

Jones and Sasser [1995] stated that the only truly loyal customers are very satisfied customers in highly competitive industry due to commoditization or low differentiation or many substitutes or low cost of switching.

H7. There will be significant differences bet- ween groups when classified into groups of satisfaction (5 Score-Very satisfaction), neutral (4 Score- Satisfaction), and dis- satisfaction (3 Score-Normal, 2 Score- Un- satisfaction, 1 Score-Very unsatisfaction).

4. Research Design and Hypothesis Test

4.1 Research Design

In this study, we investigated the effect of digital experience on brand loyalty and pur- chasing by selecting four products of fashion, auto, cosmetics and online shopping malls.

For this study, 1200 people were surveyed

and 1200 valid ones were analyzed. SPSS 24,

AMOS 23 and Process 3.3 were used for sta-

Total

Division Fre-

quency Percent Cumu- lative

Gender

male 651 54.3 54.3

female 549 45.8 100.0

all 1,200 100.0

Age

20s 150 12.5 12.5

30s 372 31.0 43.5

40s 328 27.3 70.8

50s 228 19.0 89.8

60s & over 122 10.2 100.0

All 1,200 100.0

cationEdu-

Less than High

School 170 14.2 14.2

College student 60 5.0 19.2 Professional

college graduate 150 12.5 31.7 College graduate 705 58.8 90.4 Graduate school 115 9.6 100.0

all 1,200 100.0

<Table 5> Demographic Characteristics of the Sample

tistical analysis. The customer rated overall satisfaction on a 5-point scale from very un- satisfied to very satisfied. Jones and Sasser, Jr. [1995] stated that there have been two types of loyalty of true long-term loyalty and what we call false loyalty in the highly com- petitive industry zone which is commoditi- zation or low differentiation, many substitutes, low cost of switching. The four selected in- dustries are considered highly competitive.

Therefore this study divided into three groups.

That is, from 1 point (very unsatisfaction) to 3 points (normal) was defined as dissatisfied group. Also, 4 points(satisfaction) were grouped as neutral group and 5 points (very satisfac- tion) as satisfied group.

4.2 Hypothesis Test

4.2.1 Characteristics of Research Group

The characteristics of the sample are as follows. Among 1,200 respondents, 54.3% were males and 45.8% were females. The age of

30s was 31.0%, and 40s was 27.3%. 70.3%

were from universities and colleges. We sur- veyed 300 people for fashion, auto, cosmetics and online shopping malls. A total of 1,200 persons were analyzed.

As a result, the dissatisfied group was 37.8%, the neutral group was 49.1%, and the satisfaction group was 13.2% as shown in

<Table 6>.

Division Frequency Percent Cumulative Satisfied

Group 453 37.8% 37.8%

Neutral

Group 589 49.1% 86.8%

Dissatisfied

Group 158 13.2% 100.0

all 1,200 100.0

<Table 6> Three Group of Digital Experience

4.2.2 Confirmatory Factor Analysis

In this study, exploratory factor analysis was omitted because the digital experience attributes were based on the previous stu- dies of Chung and Jung [2018]. In order to test the convergent validity of the variables, confirmatory factor analysis was conducted.

The results of confirmatory factor analysis are shown in <Table 7>.

The structural equation model is χ² = 607.778, df = 116, p = .000, χ²/df = 5.239 RMR

= .029, RMSEA = .059, GFI = .933, AGFI =

.912, NFI = .947, IFI = .957, CFI = .957 were

derived and the validity of the study model

satisfies the acceptance criteria because it

satisfies the absolute fitness index and the

incremental fitness index criteria However,

the model is not suitable for the Chi-squared

Test Value (χ²) of p = .000. Normally, if the

sample size is large, the model may not be

suitable. Thus, when the size of the sample

is large enough and the study model is quite

construct variables

Total Regression

Coefficient C.R. Composite

reliability AVE Cronbach α

Digital Experience

Aesthetic .763 Fix

.998 .973 .939

Convenience .778 28.564

Channel-to-channel consistency .770 28.204

Accessibility .739 26.880

Information quality .796 29.325

Privacy .751 27.397

Security .765 27.973

Amusement .772 28.282

Personalized .762 27.858

Drive engagement .706 25.499

Recommendation .713 25.783

Personalization .696 25.063

Loyalty

Purchasing intention .714 6.863

.956 .897 .707

Switching intention -.624 Fix

Recommending intention .683 6.839

Purchase Purchase Amount .824 Fix

.970 .942 .873

Purchase Expenditure Cost .941 15.743

χ² = 607.778, df = 116, p = .000, χ²/df = 5.239 RMR = .029, RMSEA = .059, GFI = .933, AGFI = .912, NFI = .947, IFI = .957, CFI = .957

<Table 7> Confirmatory Factor Analysis and Reliability Test

Total Digital

Experience Purchasing

intention Recommending

intention Switching

intention Purchase

Digital Experience 1

Purchasing intention .544^** 1

Recommending intention .486^** .493^** 1

Switching intention -.171^** -.167^** -.114^** 1

Purchase .152^** .237^** .324^** -.122^** 1

**Correlation is significant at 0.01 level (both sides).

<Table 8> Results of Correlation Analysis

theoretical, the value of the Chi-squared test

value and the P value do not significantly affect the fitness of the model. In the case of this study, this phenomenon can be seen with 1,200 samples. Nevertheless, since all of the remaining fit indices meet the acceptance cri- teria, there is no problem in adopting this mo- del. The criteria for the convergent validity is were as follows. Construct reliability (CR) were .7 or more. and AVE (average variance extracted) is more than .5. Also, the reliability of the

Cronbach alpha was also higher than .7. In light of this criteria, Research model was fitted.

4.2.3 Correlation Analysis

As a result of the correlation analysis to test the validity of the discriminant validity among the factors confirmed by the factor analysis, the correlation coefficient between each factor was less than .8 as shown in

<Table 8>, and the discriminant validity was

satisfied.

Path Non- standardized

estimates

Standardized

estimates S.E. C.R. P Results Digital experience → Purchasing intention .577 .687 .032 21.578 *** supported Digital experience → Recommending intention .518 1.188 .063 18.906 *** supported Digital experience → Switching intention -.186 -.263 .042 -6.242 *** supported

Recommending intention → Purchase .271 .163 .019 8.682 *** supported

Purchasing intention → Purchase .091 .105 .036 2.911 .004 supported

Switching intention → Purchase -.076 -.074 .027 -2.799 .005 supported

χ² = 56.406 df = 30, p = .000, RMR = .020, RMSEA = .062, GFI = .986, AGFI = .961, NFI = .984, IFI = .985, TLI =. 973, CFI = .987

<Table 9> SEM(Structural Equation Modeling) analysis of Digital experience to Purchase Process Chain

4.2.4 Hypothesis Test Results

When we look at the structural equation model and judge it, χ² = 69.917 df = 30, p = .000, χ²/df = 2.3318, RMR = .039, RMSEA = .033, GFI = .984, AGFI = .954, NFI = .973, IFI = .985, CFI = .984. Although χ² test value was not significant, all other indicators in- dices were fitted. Therefore, it is judged to be suitable for analysis.

Hypothesis testing shows that digital expe- rience affects positive (+) impact on purcha- sing intention and positive (+) impact on re- commending intention and negative impact (-) on switching intent and subsequently affects positive impact (+) to purchase and incase of switching intent, negative impact (-) to purchase as shown in <Table 9>.

A result of the hypothesis test, as shown in

<Table 10>, in the case of the satisfied group (5 sore - very satisfaction) of digital cumula- tive overall satisfaction, shows that digital experience influences positive purchasing in- tention (+), positive recommending inten- tion (+) and negative influence (-) on swit- ching intention. Subsequently, the recommen- ding intention and purchasing intention affects positively (+) purchasing, whereas the swit- ching intention affects negatively on pur- chase (-).

On the other hand, in the case of the neutral group (4 score-satisfaction) of digital cumulative overall satisfaction, the digital experience had a positive effect on the pur- chasing influence and the recommending in- tention, and the recommending intention and the purchasing intention have had a positive influence on the purchase. However digital experience does not have a significant influ- ence relationship on switching intention and also switching intention have no a signifi- cant influence relationship on purchase.

In the case of the unsatisfied group (3 Score-Normal, 2 Score-Unsatisfaction, 1 Score- Very unsatisfaction) of digital cumulative overall satisfaction, the digital experience had a positive influence only on purchasing intention and only the recommending inten- tion had a positive influence on the purchase.

However digital experience does not have a significant influence relationship on recom- mending intention and switching intention and also purchasing intention and switching intention have no a significant influence re- lationship on purchase.

In the case of satisfied group, H1, H2, H3,

H4, H5, H6, H7 have been supported. How-

ever if the group is unsatisfied, only H1 and

H4 have been supported, H2, H3, H5, H6

have been rejected.

Satisfied Group Neutral Group Dissatisfied Group Path

Non- standar-

dized esti- mates

Standar- dized

esti- mates

S.E. C.R.

Non- standar-

dized esti- mates

Standar- dized esti- mates

S.E. C.R.

Non- standar-

dized esti- mates

Standar- dized

esti- mates

S.E. C.R.

Digital

experience → Purchasing

intention .555 .455 .048 11.563^*** .352 .320 .054 6.471^*** .430 .339 .131 3.282^**

Digital

experience →Recommending

intention 1.089 .507 .083 13.057^*** .560 .240 .117 4.797^*** -.363 -.135 .285 -1.275 Digital

experience → Switching

intention -.377 -.195 .079 -4.764^*** -.062 -.050 .063 -.993 -.227 -.173 .139 -1.638 Recommending

intention → Purchase .174 .230 .031 5.597^*** .157 .242 .031 5.105^*** .128 .223 .059 2.170^**

Purchasing

intention → Purchase .165 .124 .054 3.068^** .155 .112 .064 2.415^** -.074 -.061 .124 -.593 Switching

intention → Purchase -.104 -.123 .031 -3.300^*** -.024 -.019 .058 -.408 .075 .064 .116 .643 χ² = 69.917 df = 30, p = .000, χ²/df = 2.3318, RMR = .039, RMSEA = .033, GFI = .984, AGFI = .954, NFI = .973, IFI = .985, CFI = .984

<Table 10> SEM(Structural Equation Modeling) Analysis of Digital Experience to Purchase Process Chain

5. Conclusion

5.1 Summary of Research

In this study, we have focused on inve- stigation of the digital experience to pur- chase process chain and the differences of the digital satisfied, neutral and dissatisfied group in analyzing the impact of digital ex- perience on purchasing intention, recommen- ding intention, switching intention and finally purchase. For the digital experience attri- butes, we used the research of Chung and Jung [2018b] and Brodie et al. [2011]. The research targets were users of fashion, auto- mobiles, cosmetics and online shopping malls.

We analyzed 300 people for each valid ques- tionnaire. In summary, the results of the study are as follows.

First, it has been proven that digital expe- rience affects positive (+) impact on purcha- sing intention and positive (+) impact on recommending intention and negative impact (-) on switching intent and subsequently affects positive impact (+) to purchase and in case of switching intent, negative impact (-) to

purchase in the Digital experience to Pur- chase process Chain (DPC).

Second, the digital satisfied group (5 score- very satisfaction) has shown same result as above. However in the case of the digital neutral group (even though 4 score- satisfac- tion in five-point scale), the digital experience have no a significant influence relationship on switching intention and also switching intention have no a significant influence re- lationship on purchase. It means that this group have that If there is a high level of attractiveness of substitute goods, there is a high possibility of switching them. It has sup- ported Jones, Jones and Sasser [1995] that there have been two types of loyalty of true long-term loyalty and what we call false lo- yalty in the highly competitive industry zone which is commoditization or low differentia- tion, many substitutes, low cost of switching.

However it has a positive effect on the

purchasing influence and the recommending

intention and aslo the recommending inten-

tion and the Second, in the case of the digital

unsatisfied group (including 3 score-normal),

the digital experience have no a significant

influence relationship on recommending inten- tion and switching intention and also pur- chasing intention and switching intention have no a significant influence relationship on purchase. Nevertheless the digital expe- rience have a positive influence only on pur- chasing intention, and only the recommen- ding intention had a positive influence on the purchase.

5.2 Discussion and Implications

The academic and practical implications of this study have been as follows. On an aca- demic level, first, digital experience has a significant effect on between recommending intention, purchasing intention and switching intention. It also supported previous study of Verhoef et al. [2009] that customers’ posi- tive digital customer experiences affect satis- faction, reliability, intention to return, inten- tion to buy, intention to repurchase, and lo- yalty. It also supported the prior investiga- tion of Brodie et al.[2011] that digital cus- tomer experiences affect ongoing intent to use. Consequently, it has been proved that Digital experience to Purchase process Chain (DPC) of the relationship between Digital Experience (DX), customer satisfaction (CS), customer loyalty (CL) and firm’s financial performance(Purchase) has been identified to be more suitable in the digital age.

Second, but unlike traditional methods un- der digital economy, 4 score-satisfaction in five-point scale (neutral group) is not true long-term loyalty, so they can change the brand at any time. furthermore 3 score-nor- mal is almost same as dissatisfied group in purchase behavior.

Third, there have been two types of satis- faction-loyalty link of true long-term loyalty and what we call false loyalty in the highly

competitive industry. The only truly loyal customers are not satisfied customers (point 4 ) but very satisfied customers( point 5) in five-point scale in highly competitive indu- stry.

Practical implications are as follows. First, the Fourth Industrial Revolution can be sum- med up as a combination of the Cyber and Physical System worlds (CPS) [Åkeson, 2016].

With the advent of this era of the Fourth Industrial Revolution, digitalization is rapi- dly advancing through all areas of industry and life. Customer journey with digitaliza- tion is looking totally different from previous customer journey. It is necessary to actively utilize digital experiences to get a high pur- chase conversion rate.

Second, unlike traditional marketing, it is

necessary for marketers to understand cus-

tomer satisfaction. Especially in a highly com-

petitive industry, a non-Very satisfaction (4

score-satisfaction in five-point measure) is

not a real loyalty and 3 score-normal is same

as dissatisfied group. Since the competitive

environment affects satisfaction-loyalty rela-

tionship, there are two different loyalty which

are true loyalty (5 score-very satisfaction)

and false loyalty (4 score-satisfaction). Yaffe

et al. [2019] also has reported that as the

forces of the Fourth Industrial Revolution

accelerate, the experience economy is boo-

ming World Economic Forum. While 78% of

millennials choose to spend money on a desi-

rable experience over something material,

the trend extends beyond just young people,

to every age bracket and socioeconomic class

but physical retail is dead from a threat of

the impact of digital disruption. Customer

satisfaction group information can be impor-

tant indicator for developing digital custo-

mer experience strategy and suitable mar-

keting actions required to raise the different

digital satisfaction levels. Identifying true lo- yalty and false loyalty is crucial to establi- shing a customer experience strategy.

Finally in order to enhance the digital cus- tomer experience, it is necessary to actively utilize long-term digital experiences strategy to increase the total satisfaction of digital experience through all of customer purcha- sing journey. It is difficult to see the effect as a one-time event. It should be scaled over the entire customer purchase process over a long period of time, which can positively affect purchase intention, recommendation inten- tion, and conversion intention. This is also why it is difficult for second-runners to over- take first-runners in a short period.

5.3 Limitations and Future Research

In this study, we have analyzed three re- presentative consumer products and found out the same result regardless of the brand and product group. First, this theory can be suggested to be somewhat generalizable to consumer products. However, for more gene- ralization, it is necessary to expand research into other industry. Second, the purchase fre- quency or purchase amount used as a depen- dent variable was not based on objective mea- surement data but dependent on the self- entry method of the surveyor. Consequently it did not remove the bias of the data that could come from the self-entry way. Finally to date, the focus has been on proving the significance of Digital experience to Purchase process Chain (DPC) of the relationship bet- ween Digital Experience (DX), customer satis- faction (CS), customer loyalty (CL) and firm’s financial performance(Purchase) under digi- tal environments, rather than focusing on Ser- vice Profit Chain (SPC). So, for the satis- fied/neutral/unsatisfactory group, we focused

on the difference analysis between paths. So statistically, significance analysis is the limit of this paper and is a future project.

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저자소개

Sang Hee Jung

Mr. Jung Sang Hee is cur- rently a Ph.D student of the graduate school of venture, at Hoseo University in Seoul.

He received a bachelor’s de- gree from Hanyang Univer- sity in Korea and a master’s degree from Stony Brook University in the United States. He is currently serving as head of the SAP CX ( Customer Experience) Solution advisory in Korea. His research interests are customer experience, digital experience, platform busi- ness and the 4

Industrial Revolution

Sang-jik Lee

Dr. Sang-jik Lee is a profe- ssor of graduate school of Ven- ture, Hoseo University, Seoul, Korea. He received his doc- toral degree in Yeungnam Uni- versity in Korea. He has pu- blished research papers in journals including the Analysis of Linkage for Social Welfare System and Skill Development in Korea, a Study on the Improvement of Korean Welfare System-Focused on the Comparison of Wel- fare and National Competitiveness of OECD countries. He also published several books including “the fundamentals of the economy“.

He consulted government organizations and

several multinational companies. His rese-

arch interest is Economic and Social Policy,

International Management and Enterprise

social responsibility.