SOFT MODEL CONSUMER ONLINE DECISION JOURNEY: … · technology, humanism had no significant effect...

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International Journal of Civil Engineering and Technology (IJCIET)

Volume 9, Issue 9, September 2018, pp. 494–507, Article ID: IJCIET_09_09_050

Available online at http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=9&IType=9

ISSN Print: 0976-6308 and ISSN Online: 0976-6316

©IAEME Publication Scopus Indexed

SOFT MODEL CONSUMER ONLINE DECISION

JOURNEY: MARKETING MIX, SOCIAL

CULTURALO, INFORMATION TECHNOLOGY

AND HUMANISM AS A MODERATING EFFECT

Irwan Christanto Edy

Department of Management,

Institute of Economic Science “Adi Unggul Bhirawa”, Surakarta, Indonesia

Riyanto

Department of Economic,

Faculty of economic education, PGRI University, Semarang, Indonesia

Heriyanta Budi Utama

Department of Management,

Institute of Economic Science “Adi Unggul Bhirawa”, Surakarta, Indonesia

ABSTRACT

This study aims to examine the moderating effects of external factors, namely

marketing mix, social cultural, information technology, humanism in the journey of

online consumer decisions. This study uses a population of banking managers/staff in

the province of Central Java, Indonesia. Research uses the surve method. Data

collection instruments with questionnaires sent by post. The number of respondents

obtained was 100 people. Data collection techniques with random sampling are data

collection methods that are done randomly. Instrument testing uses validity and

reliability. Statistical analysis with Warppls, assuming the data is not normal

distribution. The results showed that marketing mix, social cultural, information

technology, humanism had no significant effect on the process of online consumer

purchasing decisions. The contribution of marketing mix, social cultural, information

technology and humanism as moderating variables in the online consumer decision-

making process is relatively low (around 44%). In the online consumer context, the

results of this study do not support the theory of Kotler (1999) because the external

factors of online consumers do not affect online consumer purchasing decisions.

Key words: marketing mix, social cultural, information technology, humanism, online

consumer decision journey.

Soft Model Consumer Online Decision Journey: Marketing Mix, Social Cultural, Information Technology

and Humanism as a Moderating Effect


Cite this Article: Irwan Christanto Edy, Riyanto, Heriyanta Budi Utama, Soft Model

Consumer Online Decision Journey: Marketing Mix, Social Cultural, Information

Technology and Humanism as a Moderating Effect. International Journal of Civil

Engineering and Technology, 9(9), 2018, pp. 494-507.

http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=9&IType=9

1. INTRODUCTION

E-business encompasses all business activities that use information and communication

technology both between consumers and organizations and organizations to organizations

(Graaf & Muurling, 2005; Meier & Stormer 2012; Marca 2005 ). Kotler (2009) states that e-

business is an illustration of the use of electronic devices and platforms to run a business. The

development of the internet has greatly enhanced the company's ability to run businesses

faster, more accurately, through wider time and place constraints, reduced costs and the

ability to customize and personalize online consumer behavior.

The prospect of e-business in Indonesia is getting bigger, as evidenced by the value of

online shopping transactions in Indonesia, in 2012 and in 2017, has reached 736 million US

dollars (around 7.2 trillion Rupiah). The success of e-business is strongly influenced by

various factors, both internal factors of consumers and external factors of consumers.

Previous research has mostly discussed about online consumer internal factors that influence

purchasing decisions such as trust (Luo et al., 2012), motivation and hedonism (Close et al.,

2010), perceived benefits, motivation, experience (Shang et al., 2005), security (Thaw et al.,

2009). Socio-demographics such as age, income, occupation, consumption patterns, marital

status influence online consumer behavior (Zuroni et al., 2012; Gong et.al, 2013;

Moshrefjavadi et al., 2012; Osman et al., 2010; Li et al., 2010).

Online media such as websites have an important role in online consumer purchasing

decisions such as the quality of design and information quality (Zhou et al., 2009; Huang et

al., 2008). The empirical test results show that several dimensions of web media are health

risk, quality risk, time risk, shipping risk, after-sales risk, vendor credibility and security of

transactions that significantly influence online consumer buying behavior (Zhang et al., 2012;

Adnan, 2014) Likewise, experience with brands (Lodorfos et al., 2006; Becerra et.al, 2011;

Furkonudin et al. 2016 ).

Literature studies in previous studies that discuss the influence of external factors are still

limited and rare, there are only a few like Astuti et al. 2015; Santoso & Mual 2013, Azzadina

et al. 2012. Likewise, literature studies on previous research on social cultural influences on

online consumer behavior, researchers only find a little like Holleschovsky (2015), Wang et

al.,(2016). Researcher's literature study also shows the scarcity of research that discusses the

influence of information technology on online consumer purchasing decisions. Researchers

only recorded a little like Chou (2010), Ding & Chai (2015). Literature studies on the

influence of humanism on online consumer purchasing decisions show very limited and rare

findings,that do not even exist.

However, previous research is still rare which explains the role of external factors

(marketing mix, social cultural, information technology, humanist) in Consumer Decison

Journey from McKinsey (2009) in the context of online consumers. In Consumer Decison

Journey there are four stages of a consumer decision journey, namely considering, evaluating,

buying, enjoying and advocating (Court et al, 2009).

Research gap in this research is marketing mix, social cultural, information technology,

humanism in the context of online consumer behavior is interesting to study because

Indonesia is a developing country where the people are strongly influenced by social life,

Irwan Christanto Edy, Riyanto, Heriyanta Budi Utama


culture, very strong customs and the rapid development of technological science. Indonesia is

unique with its diversity. Online consumer behavior in Indonesia is not separate from social

culture, information technology development, marketing strategies and humanism. So the

problem in this study is to understand the influence of marketing mix, socio-culture,

information technology and humanism in an integrated manner on the journey of online

consumer decisions. In this study the perspective of the Consumer behavior theory

perspective (Kotler, 1999) and the Consumer Decission Journey from Mc.Kinsey (2009). This

research is to provide evidence whether in the context of online consumer behavior the theory

of consumer behavior from Kotler (1999) can still be treated practically in online business

activities.

2. REVIEW OF PREVIOUS RESEARCH

In this study approach Consumer behavior theory (Kotler, 1999) and Consumer Decision

Journey (Consumer Decission Journey) (Engel et al., 2006 ; Mc Kinsey Quartely, 2009). In

the process of consumer decision making, McKinsey et.al (2009) introduced the concept of

"Consumer Decison Journey", which travels consumer decisions through four stages:

(1)consider, (2)evaluate, (4)purchase, and (4)enjoy, advocacy, bonds (Court et al.,2009).

Previous studies that discuss the influence of external factors are still limited and rare,

there are only a few like Astuti et al. 2015; Santoso & Mual 2013, Azzadina et al. 2012.

Literature studies on previous research on social cultural influences on online consumer

behavior, researchers only find a little like Holleschovsky (2015), Wang et al.,(2016).

Researcher's literature study also shows the scarcity of research that discusses the influence of

information technology on online consumer purchasing decisions. Researchers only recorded

a little like Chou (2010), Ding & Chai (2015). Literature studies on the influence of

humanism on online consumer purchasing decisions show very limited and rare findings,that

do not even exist. The results of previous studies on the role of marketing mix, socio-culture,

information technology and humanism on online consumer behavior are still very varied as

there are studies from Hasan et al., 2008 which shows that culture does not significantly

influence consumer behavior online, while other researchers state that socio-culture has a

significant influence on consumer behavior in purchasing decisions (Siringoringo, 2004;

Ngafifi, 2014). So, the conceptual framework developed based on these two theoretical

approaches is :

Figure 1 Conceptual Model of consumer online decision journey: marketing mix, social cultural, information

technology and humanism as a moderating effect




This study uses 9 variables with the approach of the theory of "Consumer Decision

Decision (McKinsey, 2009), namely variables : Marketing mix (X1), Social Culture (X2),

Information Technology (X3), Humanism (X4), Need Recognition (X5), Search Information

(X6), Alternative Evaluation (X7), Decision of Supervision (X8), Post-Supervision (X9).

There are hypotheses to be tested in this research are: H1 : The marketing mix moderates significantly the impact of recognition of need on

information retrieval

H2 : The marketing mix moderates significantly the impact of information retrieval on

alternative Evaluations

H3 : The marketing mix moderates significantly the impact of alternative evaluation on

purchasing decisions.

H4 : The marketing mix moderates significantly the impact of purchasing decisions on post-

sponsorship

H5 : Social Cultural moderates significantly the impact of recognition of need on information

retrieval

H6 : Social Cultural moderates significantly the impact of information retrieval on alternative

Evaluations

H7 : Social Cultural moderates significantly the impact of alternative evaluation on purchasing

decisions.

H8 : Social cultural moderates significantly the impact of purchasing decisions on post-

sponsorship

H9 : Information technology moderates significantly the impact of recognition of need on

information retrieval

H10 : Information Technology moderates significantly the impact of information retrieval on

alternative Evaluations

H11 : Information Technology moderates significantly the e impact of alternative evaluation on

purchasing decisions.

H12 : Information Technology moderates significantly the impact of purchasing decisions on

post-buying

H13 : Humanism moderates significantly the impact of recognition of need on information

retrieval

H14 : Humanism moderates significantly the impact of information retrieval on alternative

Evaluations

H15 : Humanism moderates significantly the impact of alternative evaluation on purchasing

decisions.

H16 : Humanism moderates significantly the impact of purchasing decisions on post-sponsorship

3. RESEARCH METHODOLOGY

The research was conducted in Indonesia. Object of research in banking institutions that have

implemented e-business in their business activities. The population of this study is banking

managers as consumers of online banking products, with characteristics experienced in online

marketing. This research sample is a banking marketing manager in Central Java, Indonesia.

The unit of analysis is the individual. Data collection methods are carried out with surve. The

sampling technique was purposive sampling method because the respondents were

determined by certain criteria by the researcher. Data collection instruments in the form of a

closed questionnaire with a Likert scale where there are 5 points. 100 respondents were

chosen. The selected data were tested for instruments, namely reliability and validity testing.

Reliability and validity aim to ensure valid and consistent data quality (Sugiyono, 2013).

This study uses data analysis by SEM (Structural Equation Modeling) to test hypotheses

concerning the relationship between latent variables in research. SEM analysis in this study

uses WarpPlS because there are less than 200 respondents and the data is not normally



distributed (according to SEM requirements with WarpPLS). SEM analysis with WarpPLS

uses a two-step approach where the analysis is preceded by measurement analysis to confirm

whether the indicator indicators used in this study really represent each latent variable from

the research model. Then a structural model analysis is performed to test the hypothesis of the

hypothesis proposed in this study.

4. RESULT

4.1. Descriptive Statistic

This descriptive statistic describes the characteristics of respondents, viewed from several

variables such as: gender, age, status, satisfaction in online transaction :

Table 1 Resume of Background Respondent

Information Sum Prosentase

Gender

Male

Female

30

70

30%

70%

Age

< 20 years

>= 20 years

46

54

46%

54%

Status

Staff

Manager

Online Transaction Satisfaction

54

46

54%

46%

Satisfied

Not satisfied

netral

85

13

2

85%

13%

2%

Source : author’ work in 2018

Characteristics of respondents (table 1) show that most respondents are women, aged over

20 years, with staff positions and feel satisfied in online business. These characteristics

illustrate the objectivity of data which is one of the requirements for further data analysis.

4.2. Validity and Reliability Test

Table 2 Validity and Reliabity Test Results

Crobach Alpha Item of Valid

Marketing Strategy 7P Mix (X1) 0,7694 21

Socio-cultural (X2) 0, 6740 4

Information Technology (X3) 0,6602 5

Humanism (X4) 0,7684 5

Recognition of Needs (X5) 0,8866 5

Search Information (X6) 0,8661 5

Evaluation of the Alternative (X7) 0,8365 5

purchasing decisions (X8) 0,7611 5

post-sponsorship (X9) 0,7616 5


Based on the reliability test shows that data quality is good, data meets reliable and valid

criteria, because all Cronbach Alpha values are greater than 0.6. Data that has fulfilled the

reliability and validity testing has good quality and can be used for further analysis.




4.3. Data Distribution

Data patterns show the distribution of research data, and data distribution determines the use

of data analysis tools. The results of this research are distributed data can be explained in the

picture below:

Figure 2a

Figure 2b

Figure 2c

Figure 2d

Figure 2 Data Point and Regression Line or Curve

Figure 2a shows the regression line of the relationship between variables Recognition of

Needs (X5) and Search Information (X6) that are not linear. Figure 2b shows the regression

line of the relationship between variables Search Information (X6) and Evaluation of the

Alternative (X7) that are not linear. Figure 2c shows the regression line of the relationship

between variables Evaluation of the Alternative (X7) and purchasing decisions (X8) that are

not linear. Figure 2d shows the regression line of the relationship between variables

purchasing decisions (X8) and post-sponsorship (X9) that are not linear.

Data distribution is depicted by regression lines or curves of various variable

relationships, and based on the figure above (figure 2) shows a non-linear relationship. Data

distribution with non-linear relationship curves means that data distribution is not a straight

line. Based on the data distribution, the analysis can be used to test the model using structural

model with the Partial Least Square (PLS) method with Warppls software. Structural model

with the Partial Least Square (PLS) method are soft model data analysis that requires non-

linear data distribution.



4.4. Structural model

Data analysis techniques used a structural model with the Partial Least Square (PLS) method.

The structural model in this study will be analyzed using the WarpPLS 3 program. In this

study using SEM (Structural Equation Modeling) because SEM is a type of multivariate

analysis in social science, where multivariate analysis is the application of statistical methods

to analyze several research variables simultaneously or simultaneously. The purpose of using

multivariate analysis is (1) to confirm, and (2) to explore. Multivariate confirmation analysis

is used to test hypotheses developed based on existing theories or concepts. In this study, PLS

SEM is used because (1) research is exploratory or extends existing theories, (2) Aims to

identify the main determinant variables or predict certain constructs, (3) structural models are

relatively complex (many constructs and many indicators), (4) data characteristics not

normally distributed, 5) relatively small sample size, (6) PLS SEM can achieve high statistical

power even though the sample size is small, (7) PLS SEM can measure latent variable scores

with better approach

The Warpls program can take into account both linear and linear relationships at once.

Kock (2010) states that the Warpls program can identify nonlinear relationships between

latent variables and correct path coefficient values based on these relationships. By

considering the highest validity coefficient for each latent variable, the results of data analysis

with SEM-PLS with WarpPLS provide the following structural model:

Figure 3.Soft Model Consumer Online Decision Journey: Marketing Mix, Social Cultural, Information

Technology and Humanism as a Moderating Effect

The fit model of the structural model is determined based on the fit indices and P values

model to display the results of three fit indicators, namely Average Path Coefficient (APC),

Average R-Squared (ARS) and Average Varience Inflation Factor (AVIF). The p value given

for APC and ARS is calculated by estimating resampling. The fit model of WarpPLS 5.0




program can be seen from the general output, namely: (1) Average path coefficient (APC) has

a value of P <0.05, (2) Average R-Squared (ARS) has a value of P <0.05, (3) Average

Adjusted R-Squared (AARS) has a value of P <0.05, 4) Average Block Variance Inflation

Factor (AVIF) has a value of <5 and ideally 3.3. Based on the calculation of the WarpPLS

program, the model fit values are as follows

Table 3 Model Fit Indices dan P-values

Indicator P-values Criteria Resume

1 APC 0,002 < 0,05 good

2 ARS 0,015 < 0,05 good

3 AVIF 1,680 < 5 good


Based on the three indokator (Model fit indices and P values) it can be concluded that the

model is good (fit), has the meaning that the model can be used as a confirmation theory

4.5. Hypothesis Testing

Hypothesis testing based on P-Values, if the value of P-Value count smaller than 0.01 (0.01

significant level) then it is said that the hypothesis is accepted, but otherwise the hypothesis

will be rejected.

Table 4 Hypothesis Testing

Hypothesys Path Coefisien (β) P-value Criteria Resume

H1 X5 X6 moderated X1 0,08 0,30 > 0,01 Not Significant

















Based on the results of these tests show that marketing mix, social culture, information

technology and humanism have no significant effect as a factor that moderate the process /

stages of consumer online travel decisions. Meanwhile, based on the big coefficient of

influence of moderation (β) then there are three major moderation influencing online

consumer decision decision that is: 1) influence of moderation marketing mix to relation of

evaluation of laternatif (X7) and purchasing decision (X8), 2) influence of social culture

moderation to (X7) and purchasing decisions (X8); 3) the influence of moderate humanism on

the information-seeking relationship (X6) and alternative evaluation (X7).



4.6. Coefficient of Determination

Coefficient Determination or R2 (R square) shows the contribution of independent variables

and moderation variables to the dependent variable can be presented as follows:

Table 5 Coefficient Determination or R2 (R square)

Relationship with variable R2

1 Influence X5 to X6 moderated X1, X2, X3, X4 0,44






The most influential R2 is the influence of recognition (X5) on information search (X6)

moderated by mix marketing (X1), Social Culture (X2), Information Technology (X3) and

humanism (X4). This shows that the contribution of the greatest moderation variables occurs

at the stage of recognition of needs and information search by online consumers. In the

introduction phase of the need to search for information of mix marketing factor (X1), Social

Culture (X2), Information Technology (X3) and humanism (X4) give a big influence. R2 of

0.44 means that the search of information (X6) is influenced by 44% by the recognition

variable (X5) that is moderated by the marketing mix (X1), Social Culture (X2), Information

Technology (X3) and humanism (X4), and 56 % influenced by other variables not examined

in this study. Another bigger factor could be internal online consumer factors such as

personality, psychology, motivation or others.

4.7. Discussion

The results of this study contradict the theory of consumer behavior (Kottler, 1999) which

states that social, cultural, and technological factors influence consumer behavior. Kottler

(1999) theory of consumer behavior refers to the context of offline consumers not specifically

referring to online consumers. The findings of this study provide evidence that consumer

behavior in online contexts is different from consumer behavior offline. Kottler's (1999)

consumer behavior theory cannot be used as a guide to understanding online consumer

behavior.

The results of this study indicate that marketing mix, socio-culture, information

technology and humanism have no significant effect on the online consumer decision-making

process, these findings are consistent with some previous studies such as Tjahjono (2013)

which states that marketing mix and social cultural environment does not affect consumer

purchasing decisions. Setiawan (2014), Sukotjo and Radix (2010) stated that marketing mix

has no significant effect on consumer purchasing decisions. Jarvenpaa et al., (1999) states that

culture does not affect consumer decisions online. Koufaris (2002) also states that information

technology does not affect online consumer behavior.

The coefficient of determination of the influence of marketing mix, socio-culture,

information technology and humanism on the process of online consumer decision travel is

still very low, which can be seen from R2 (R Square). R

2 is the biggest influence on the need

to identify information that is moderated by marketing mix, socio-culture, information

technology and humanism by 44%, while the influence of other factors not examined in this

study of the consumer decision process is 56%. Other factors that are not examined have a

greater influence on the process of online consumer decision making.




5. CONCLUSIONS

This study aims to measure the influence of the online consumer external environment,

namely marketing mix, social culture, information technology, and humanism on the journey

of online consumer decisions. The results of this study indicate that marketing of mix, socio-

culture, information technology and humanism as moderating variables does not have a

significant effect on the journey of online consumer decisions.

The contribution of marketing variables to mix, socio-culture, information technology and

humanism as moderating variables in the consumer decision travel process is still low at

around 44%, which means that these external factors have not significantly affected the online

consumer decision journey, and 60% are likely to be influenced by other factors which are not

examined such as internal factors such as personality and psychology / psychology. Therefore

recommendations for further research are examining the internal factors of online consumers

such as personality and psychology / psychiatry.

The results of this study do not support Kottler's (1999) theory which states that external

factors in consumers such as socio-culture, information technology, influence on consumer

behavior. In the context of online consumers, the results of this study are contrary to the

theory. The results of this study indicate that Kottler's (1999) theory does not apply to online

consumers

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