Indonesian Millennials’ Behavior Intention To
Online Shopping Through Instagram
Andre Hasudungan Lubis, Wan Rizca Amelia, Sari Nuzullina Ramadhani, Aditya Amanda Pane, Solly Aryza
Abstract: As the favored mobile application platform among young age, Instagram offers an attractive features and more friendly to be used. Instagram is a social media that usually used for online shopping purposes, such as marketing, brand exhibition, and advertisement. Millennial are the majority of online shopper, due to their activities mostly connected to the Internet and smartphone uses in daily. Hence, it is crucial to be know how and what is their attitudes and behavior in online shopping. The study employs the Theory of Planned Behavior (TPB) to explore the viewpoint of Instagram behavior intention to use. Variables that used in this research are: attitude, subjective norm, perceived behavioral control, and trust. SEM is used as the means of data analysis technique. The total samples of the survey were covered about 152 participants from Medan, Indonesia. The result pointed out that perceived behavioral control is not significant to influencing behavioral intention to online shopping. However, the other predictors are positively significant. Respondents assumed that there are some difficulties or issues regarding Instagram usage for online shopping.
Index Terms: Millenials, Online-shopping, Theory of Planned Behavior, Instagram, Indonesia.
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1.
INTRODUCTION
Information and Communication Technology (ICT) has become flourished in business performance [1]. The utilization of social media in business is perceived to be the successful for marketing the product to customer [2]. The continuance of using social media as a tool for business environment has been enhanced lately [3]. For instance, Facebook is able to provide a trust to the customer for buying product. The website service quality becomes the perceived benefit of customer which becomes the factor that affecting their trust towards the product [4]. Another social media application that has been used for online shopping purpose is Twitter. The application has been employed as the marketing tool and communication medium among customer and marketers. Customer’s attitude, normative beliefs, and perception in doing behavior become the factor that influencing their behavioral intention to buying in online procedure [5]. Moreover, YouTube also can be used for fulfilling advertising needs. The usefulness of YouTube become the factor that impacting customer intention to purchase some products in online fashion [6]. Admittedly, social media has a strong force to become an aid for online shopping [7], negating lacks of privacy and security [8]. In several countries, online shopping usually chosen by young age with certain considerations [9],[10],[11],[12]. They usually use social media to explore the product and its characteristic before purchasing [13]. Social media is preferability to use as the manner to online shopping among them [14], [15]. In Indonesia, Instagram is a social media which is prevalent to use among youth or millennial [16]. Complementary to this, Instagram is a social media application that is often to use for online shopping purposes [17].
However, a report from Statista website of statistics databases in the economic sector, Indonesia has a low rate of online shopping activities caused by certain factors. Trust is one of the factor that influencing the intention to online shopping among them, especially millennial [19]. Many researcher from different disciplines have been conducted studies regarding the issue of online shopping activities. Several theories or models also been applied, including Theory of Planned Behavior (TPB), Technology Acceptance Model (TAM), and Theories of Reasoned Action (TRA) as the frequently used for research purpose [20]. Hence the paper used TPB to explore consumers' behavior intention of online shopping among young age. Refer to TPB, there are three factors impacting individual behavior intention, namely attitude, subjective norm, and perceived behavioral control [21].
2 LITERATURE AND HYPOTHESES
2.1 Instagram
Instagram has impressive features for online sharing pictures or videos. The application allow users to connected publicly thus they can access other users' profiles and giving like, comments and sharing photos or videos to other platforms. According to the Omnicore statistics data, there are more than 1 billion of total number active Instagram users monthly, then about 72% of them are teens. The data also explained that there are more than 25 million account were used as business purposes and more than 2 million among them are monthly advertisers. Thus, it is illustrated that Instagram is the media that mainly used for business purposes. Instagram is the frequently used by millennial for shopping [17], this also supported by researcher [24], Instagram is preferable application to deals with online shopping among young age.
2.2 Theory of Planned Behavior (TPB)
The theory firstly introduced by Icek Ajzen in 1985, usually used to explore what is individual’s thought to decide in performing tasks or take steps under his/her willing control. Ajzen also added that the behavioral intention of an individual is the most influential factor in making a decision to take a particular action or leave it [26]. As stated before, TPB explained that individual behavioral intention is influenced by the attitude and subjective norms. Additionally, behavioral control is also the factor that engaging individual intention. Besides, in term of online shopping context, consumer trust is
________________________
Andre Hasudungan Lubis, Faculty of Engineering, Universitas Medan Area, Indonesia. Email: andrehasudunganlubis@uma.ac.id Wan Rizca Amelia, Faculty of Economic and Business, Universitas
Medan Area, Indonesia. Email: wanrizca@staff.uma.ac.id Sari Nuzullina Ramadhani, Faculty of Economic and Business,
Universitas Medan Area, Indonesia. Email:
sarinuzullina@staff.uma.ac.id
Aditya Amanda Pane, Faculty of Economic and Business,
Universitas Medan Area, Indonesia. Email:
adityaamanda@staff.uma.ac.id
Solly Aryza, Faculty of Science and Technology., Universitas Pembangunan Panca Budi, Indonesia. Email:
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the additional factor to influencing consumer intention [27].
i. Attitude (AT)
AT is the first indicator that influencing individual behavioral intention (BI). AT and BI is different, AT presents a preparedness for actions that lead to BI. A positive AT towards the behavior usually becomes the determinat factor to be chosen by the individual to behave in his/her life. Therefore AT is a vehicle in guiding an individual to BI [28]. In online shopping, previous studies have been approved that AT positively influencing BI, which in line with the theory. A study conducted by researcher [29] resulting a similar outcome to the theory. The variable AT has a positive coefficient indicates a direct relation towards BI to purchase online among Brazilian consumer. Then, a research by reference [30] regarding Korean consumers’ behavior of cross-border online shopping also has an equal outcome. The study accepted the hypothesis that AT positively influence cross-border online shopping intention.. Similarly, another finding also appeared from a study by [31]. The result indicated that AT has a positive influence toward on repurchase intentions WOM and e-WOM through social media. Moreover, in the study reported by reference [32], AT has a positive effect on consumers’ online shopping intention. The research by reference [33] also supported that AT is positively influencing sustainability of BI among consumer. Therefore, the study posit that The attitude (AT) toward on-line shopping is positively influencing consumers’ behavior intention (BI) as the first hypothesis.
ii. Subjective Norm (SN)
The second factor that counts to be influencing BI is SN, which is a variable that explain individuals’ perception of social thoughts that will support or not support him/her in performing a task [28]. Similar to the first factor (AT), relationship between SN and BI also have been studied lately to verify the theory. A study conducted by reference [34] supported that SN variable was significantly related to youth intention to perform an online purchasing. Moreover, a report by reference [35] argued that SN are the factors that commonly involves when consumers’ BI to purchasing product through online. Researcher [36] stated that SN has a strong influence on consumers’ intentions to online shopping. Then, researcher [37] also agreed that besides AT, SN also presumed to be impact factors towards BI. Researcher [32] also stated that SN is the factor that influencing BI along with AT variable. Hence, the study posit the second hypothesis, subject norm (SN) is positively influencing consumers’ behavior intention (BI).
iii. Perceived Behavioral Control (PB)
PB is the factor describes individual’s perception of ease or difficulty in performing a behavior. Behavioral control relates to beliefs about the availability of supports and resources or barriers to performing task [28]. Researchers have been test the relationship between PB and BI. The study by reference [38] stated that besides SN and TR, PB also becomes the variable that related to BI. Researcher [39] proposed a study regarding online clothing shoppers. The research supported all the hypotheses, and one of them is explained that PB positively impacts online purchase intentions. According to researcher [40], the study reported that PB has an impact on the BI of the consumption in online shopping. Then, a research conducted by reference [41] stated that PB is affecting BI along with other factors (i.e. AT and SN). Similar to AT
variable, reference [33] also stated that PB is one of the accomplished variable that positively influencing sustainability of BI among consumer. Therefore, in this study the third hypothesis is proposed regarding how PB is influencing BI.
iv. Trust (TR)
In traditional buying-selling process, TR is prominent factor, but when it’s organized in online fashion, it become fundamental. Online shopping is vulnerable; this is due to the shortcomings of Internet security, causing insecurity and anxiety perceived by consumer [42]. According to researcher [43], adding some extra variables in TPB able to produce a better predictive capability of the theory. Many researches have been conducted studies related to TR as the additional variable in TPB, especially in online shopping. Researcher [44] conducted a study that summarized online purchasing BI is influenced by TR. Then, study by researcher [45] concluded that TR has a significant influence towards intention of online consumers. In accordance with a report by [46], TR is obviously impacting BI. This in line with study of reference [47], stated that trust is the core in influencing online means purchase intention. Hence, the study posit the last hypothesis, Trust (TR) is positively influencing consumers’ behavior intention (BI). The study proposed the research framework based on the TPB model and literatures. Figure 1 illustrates the research framework of the study.
3 METHOD
3.1 Research Samples
The survey was conducted through online transmission to respondents in Medan, Indonesia. About 300 questionnaires have been disseminated among young age between 13 to 19 years old of millennial. A total 250 respondents are sent back the respond both buyer and not in online shopping through Instagram. Then, about 153 total of participant are the online shopper. All the participations in the survey were voluntary and anonymous.
3.2 Data Collection
Instrument of the study is adopted from researcher [48] with total 15 questions. The question including: AT variable with 4 questions, SN with 4 questions, PB variable with 2 questions, TR variable with 2 questions, and BI with 3 questions. The questionnaire was split in two main sections. Firstly, respondents are asked the question regarding demographic of respondent, including gender, age, and educational level.
AT
H1
H2
H3
H4 SN
PB
TR
BI
Then, the next section of the questionnaire includes four parts, so as to examine responses on four dimensions. These four parts included attitude toward on-line consumer behavior, subjective norms, perceived behavioral control and on-line shopping behavioral intentions. Respondents were asked to express their agreement with each item of the questionnaire based on the Likert-type scale, namely strongly disagree, disagree, neutral, agree, and strongly agree.
3.3 Technique of Data Analysis
The study employs Structure Equation Model as the data analysis technique to test the hypothetical relationship between variables in the proposed conceptual. According to reference [49], SEM is organized in two major steps, namely measurement models and structural models. The first step, the research analyzes the measurement model using Confirmatory Factor Analysis (CFA) to assess the goodness-of-fit model and the validity and reliability of instrument. Then, the research model and proposed hypothesis will be evaluated using path coefficients in the second step as a means to analyze structural models.
4 RESULTS
4.1 Overviewed of Data Gathered
This data were gathered among respondents with total 152 samples. For the gender, there are 86 participants are female (56.2%) and total 67 among them are male (43.8%). Furthermore, most of the respondents are 19 years old and a few of them are 17 years old. Moreover, in terms of educational level, A total of 90 respondents are in Junior High School level (58.8%), then about 41 participants are higher education students, and only 22 of them are Senior High School level (14.4%). Table 1 summarizes of data gathered in the study.
TABLE1
DEMOGRAPHIC OF RESPONDENTS.
4.2 Data Analysis and Results
i. Measurement Models
CFA is used to test relationships between different constructs in a conceptual model. To assess the measurement model in CFA,
the first step is to consider the fit measurement model and then evaluate the validity and reliability of the measurement model. This study used several criteria to measure the compatibility between the hypothesized model and the observed covariance matrix. According to reference [49], the criteria including Chi-Square (χ2), CMIN / DF (Normed Chi Chi-Square), GFI (Goodness Fit Index), AGFI (Adjusted Goodness Fit Of Index), RMSEA (Root Mean Square Error of Approximation, NFI (Normed Fit Index), IFI (Incremental Fit Index), and CFI (Comparative Fit Index). The requirements for fit model criteria in the research illustrated in Table 2.
TABLE2
SUMMARY OF GOODNESS OF FIT INDICES.
ii. Validity and Reliability
The study used discriminant validity to determine the validity of instruments, which is calculated by comparing the square root value of Average Variance Extracted (AVE). Discriminant validity can be said to be achieved if the AVE value is greater than 0.5 [49]. Furthermore, Construct Reliability (CR) is utilized as a method for testing reliability of instruments, which uses CFA to get the Standardized Loading value. A good CR value is considered if the value is greater or equal to 0.70 [49]. Cronbach’s alpha test in the research was performed for the each variable and resulting a good level of reliability (α >0.80) ranged from 0.906 to 0.955. The model assessment is attached in Table 3.
TABLE3
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According to Table 3, the factor loadings of every items are passed the cut off value (0.5) [50]. Then, Table 3 also shows that CR value of each variable has a greater value than 0.5, hence the instrument has a suitable reliability. Furthermore, the AVE values are above than 0.7 as stated in Table 3. Besides, a good discriminant validity determined by the coefficient correlation value among variables should lower than 0.90 [51]. Table 4 illustrates the correlation score between entire constructs, and then it shows the scores are below than 0.90 and indicates a sufficient validity.
TABLE4
CORRELATION AMONG CONSTRUCTS.
** Correlation is significant at the 0.01 level (two-tailed).
iii. Structural Models
The modeling process in the study was carried out by evaluating the statistical significance of each structural parameter value. Path coefficients obtained from SEM are used to verify the hypothesis, after passing through the model's conformity with CFA. Path coefficients are standardized regression coefficients that show the direct effect of an exogenous variable on endogenous variables in the path model. Furthermore, the value of significance (p value) is used to accepted or rejected the hypotheses [49]. Table 5 shows the result of testing the hypotheses establishing the relations among factors.
TABLE5
RESULT OF STRUCTURAL MODEL ANALYSIS.
According to Table 5, customer’s attitude is positively influenced the behavioral intention in online shopping, based on its significance level (β=0.717 and p=0.000<0.05). Therefore, Hypothesis 1 is supported. Then, Subjective Norm factor has a tolerable level of significance (β=0.142 and p=0.000<0.05), which shows that a consumer’s online shopping behavior intention is affected by social relationship. Hence, Hypothesis 2 is accepted. Moreover, the study also pointed out that consumers’ trust to online shopping is influenced their behavioral intention, this is verified by the significance level (β=0.295 and p=0.000<0.05) of the variable and support the Hypothesis 4. In contrast, Hypothesis 3 is rejected with a poor of significance level (β= -0.026 and p=0.533>0.05). Thus, it is indicated that consumers’ intention to online shopping is not related to their behavioral control.
4
CONCLUSION
TPB is again to be an applicable measurement of behavioral intention in online shopping as proposed by the study. There are 3 hypotheses were supported and just one was rejected. Attitude is the most robust influence in online shopping intention. Customers believe that Instagram is very decent and worthy for shopping purposes. Thus, the result is following to the past studies [29],[30],[31],[32],[33] for the agreement of relationship between attitude and behavioral intention. Then, the second factor of the study (i.e. Subjective Norm) is presumed to influencing customers to use Instagram for shopping. Friends and colleagues are found to encourage them in online shopping activity. Similar to attitude, outcome of the second independent variable is standing with other previous researcher [32],[34],[35],[36], which is concluded that Subjective Norm is influencing the Behavioral Intention. Next, Trust as the additional variable of TPB model is assumed to significant influence on online shopping behavior among millennial. Customers hold that Instagram has good operational features and has good integrity and transparency for shopping online. The result is in line with other past studies [44],[45],[46],[47], which is clarified that trust is positively influencing behavioral intention. However, there is a surprisingly result of the study. Perceived Behavioral Control as the third independent variable is mediocre and indicates a contrast outcome. Customers think that there is no such clarity about control the information about online shopping via Instagram. The finding has a similarity to the study by researcher [52], which is reported that PB is not significantly affecting on online shopping. In the same way, the outcome also linear with reference [53], this is clarified that PB is not a significant predictors of BI. So it can be concluded that consumers thought that it is difficult to control themselves while online shopping by using Instagram. Therefore, consumers should have much understandings and information regarding Instagram policy and procedure for business purposes.
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