Understanding Consumer Intention in Online Shopping
Creators: name: MULJONO, WIRYANTA
name: Setiyawati, Sri
name: Sudarsana
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Business
subject: #Understanding Consumer Intention in Online Shopping

description: Understanding Consumer Intention in Online Shopping Wiryanta Muljono1*, Sri Setiyawati2, Sudarsana3, Priyanka Pertiwi Setiawati4 1 Ministry of Communication and Information Technology, Jakarta, 10110, Indonesia 2 Department of Management, Faculty of Economics and Business, Universitas Prof. Dr. Moestopo (Beragama), Jakarta, 12250, Indonesia 3 Department of Sociology, Faculty of Social and Political Sciences, Universitas Sebelas Maret, Surakarta, 57126, Indonesia 4 Department of Ocean Engineering, Institut Teknologi Depuluh Nopember, Surabaya, 60111, Indonesia ABSTRACT Service quality, shipping, and attitude have been considered a critical factor that affects intention consumers in online shopping. Moreover, we investigate whether service quality, shipping, and attitude simultaneously affect intention in online shopping. This survey was conducted by distributing questionnaires to 400 online shopping consumers. These empirical findings provide one, among several others, insight to startups in developing service quality and shipping. The authors proposed a service quality construct into the online shopping consumer behavior. However, they ultimately concluded that the employed service features produced a significant influence on the continuance intention of online shopping. The Startup company knew who its consumers can differentiate products and services which are offered by the company and others. The limitation of the present study originates from the research scope which revolves around consumer's perception of service quality and shipping on a startup company. The question remains if the startup's unresolved negative performance could overwrite the perceived value and make consumers disposed to reluctant to shop online Keywords: service quality, shipping, attitude, intention, consumers, online shopping JEL Code: D13, I31, J22, K31 ___________ * Corresponding Author at Ministry of Communication and Information Technology, Jalan Merdeka Barat No. 9, Jakarta 10110, Indonesia. E-mail address: wiry002@kominfo.go.id or wiryantamuljono@gmail.com (author#1), srisetiyawati84@gmail.com (author#2), sudarsana099@gmail.com (author#3), priyankapertiwisetiawati@gmail.com ((author#4) 1. Introduction E-commerce in Indonesia has become increasingly important in all market segments. Indonesia’s 2020 e-commerce reach $40 billion. This year, Indonesia’s online retail was higher at $17 billion compared to 2019’s $23 billion, which followed suit. This indicates overall sales value to surpass estimate, driven by a new cohort of e-commerce users amid the Covid-19 pandemic. This condition is due to the increasing number of smartphone users in Indonesia, reaching 81.87 million users in 2020 (Muljono, et.al., 2021). Most retail goods traded via e-commerce are produced by small and medium enterprises (SMEs). Furthermore, the majority of businesses in Indonesia are SMEs, which represent 99% of the operating businesses (Das et al., 2018). The sharing economy model has predicted considerable growth in e-commerce. For example, GoJek in partnership with SMEs contributed a transaction volume of $ 1.2 billion in 2017 (Demographic Institute, 2018), which means that e-commerce in the SME sector is expected to grow considerably. The e-retail industry has been developing very rapidly and changing prior distribution systems. Hence, e-retail has higher opportunities to improve their performance if they have strong logistics capabilities (Cho et al, 2008), which has led to partnerships between startups that collect SME products to be marketed online and existing shipping services to deliver goods to consumers. As a consequence, service quality (SQ) and shipping have significantly inﬂuence attitude and intention of online shopping (IOS) and have emerged as a competitive advantage. The most favorite website to visit by Indonesian internet users are apparently online shopping sites, which are Shopee, Tokopedia, and Bukalapak (iPrice, 2020). This means that the service quality of online shopping sites is more innovative. This is also offset by courier services for shipping goods purchased online. Shipping services are required to implement and improve the service offered to fulfill various consumer needs and implement innovative solutions. It does not mean there are no gaps in online shopping. Previous research, like Dai et al. (2013), pointed out the shortcomings, that lack of access to sales associates on e-commerce is considered another restriction. Indeed, the critical aspect could have implications for declining consumer intentions of online shopping. Witnessing the strong growth of e-commerce, academics have directed increasing attention to e-commerce research because e-commerce provides diverse goods, methods of payment, and delivery for items purchased through delivery services, which has led to a number of challenges. E-commerce websites and the presence of the website-based app were initially thought to be the drivers of success, SQ issues to stand out. When consumers could not complete transactions, products were not delivered on time, the viability of e-commerce websites was jeopardized, with the chance to be abandoned by consumers (Parasuraman, et al., 2005). If e-commerce websites are to be accepted by consumers, start-up companies must shift the focus to e-service quality rather than an electronic transaction process. The phenomenon of e-service as a novelty of technology adoption is an interesting and challenging issue to be studied. Inconsistencies are observed between attitude and IOS models related to e-commerce (Li &amp; Petrick, 2008) because each study is based on different objects and settings. This condition provides an opportunity to design alternative models that are able to describe the phenomenon to be observed. A model alternative is built based on four variables, namely, SQ, shipping, customer attitudes, and IOS, which can be explained by the previous factors. This research contributes to understanding SQ, shipping, and its attributes either directly or through attitudes towards IOS. To provide a clearer picture of the relationships among SQ, shipping, and attitude on the IOS. 2. Literature Review The relevant literature for this study is discussed in this section, based on literature reviews. There are two main areas of literature used in the paper. The first is related to attitude and IOS. Then, the SQ offered by e-commerce companies and the shipping options of goods purchased. 2.1. Service quality The SQ becomes the associate’s adequate activity for promoting to seek out and analyze data regarding consumer’s needs, wants, and perceptions relating to the product offered. SQ refers to providing service that meets or exceeds consumers’ expectations (Parasuraman et al., 2005). Indeed, this definition was most cited by the other researchers for outlining SQ. Today, consumers have high expectations for startup firms to offer them what they require, once they wish it. Obviously, digital remodel from conventional SQ to electronic SQ is more beneficial for consumers. Service quality is known as SERVQUAL, which contains ﬁve dimensions: reliability, responsiveness, assurance, empathy, and tangibles have used as a measurement (Parasuraman et al., 2005). Despite the universality of the ﬁve dimensions of SERVQUAL, they do not constitute all of the dimensions appropriate for measuring all aspects of a website due to the various characteristics and features of SQ on the website displayed. Culiberg (2010) argued that these ﬁve dimensions are industry-speciﬁc and cannot be applicable to any service industries without modiﬁcations. The findings of Buhmann &amp; Brønn (2018), a behavioral intention prediction model show that SQ effectively influences consumer attitudes. However, these reported findings are not without conflicting results. A study on consumer behavior and service quality (Zhou et al., 2019) shows that consumers' perceptions of SQ can fluctuate due to the psychological influence of consumers. To apply the SQ framework to the current analysis, we have ensured that the choice of dimensions of the framework is compatible with the SQ on the website displayed. Previous studies like Eid (2011) have investigated the relationship between consumer loyalty and therefore the purchase of products in online shopping, with the treatment of 5 indicators, like website navigation, product information, product availability, timeliness of delivery, and simply return back. Later, Pearson et al. (2012) demonstrate that 2 key dimensions, particularly perceived SQ and perceived information quality of knowledge, each absolutely influences IOS. Perception of SQ and information quality are both closely related to reliability and responsiveness. Reliability is the ability to perform the services in a dependable, accurate, and totally accountable manner, without access failure (An &amp; Noh 2009). Responsiveness is the willingness to respond quickly and in real-time to consumer requests (Zhou et al., 2019). Responsiveness involves a willingness to respond quickly, keeping consumers informed regarding the availability of goods, delivery services, way of payment, and a set price. Assurance involves the consumers’ trust in the website. Trust in the start-up website depends on the consumers' notion of security of however the website manages their personal information (Flavián &amp; Guinalíu, 2006). Websites will increase the trust of the consumers by enhancing website system security (Eid, 2011). Privacy is the most serious issue is attracting a lot of online potential consumers and retains current consumers (Park &amp; Kim, 2003). Empathy is outlined by creating a relationship between consumers and sellers via web platforms, smart communication, and understanding the wants of consumers. According to Lundstrom &amp; Dixit (2008), tangibility is defined as the appearance of a website or application, the Android software package, straightforward and various options, and attention-grabbing content. For the needs of this study, SQ is measured by SERVQUAL modiﬁcations, particularly reliability, responsiveness, assurance, empathy, and tangibility. Accordingly, we have proposed the following hypothesis: Hypothesis 1. There is a positive and meaningful relationship between SQ and ATT Hypothesis 2. There is a positive and meaningful relationship between SQ and IOS 2.2. Shipping Shipping in online shopping is a service used by start-up companies that sell goods over the internet. Thus, start-up companies have to hold the proper shipping partner. Startup ought to notice faster, cheaper, a lot of economical shipping resources in order to stay up and meet consumer expectations. That is where the shipping service companies play a role in partnering with start-up companies and goods suppliers. Startup companies need to take an omnichannel approach to parcel and deliver goods. This is often to consolidate consumer orders across all of the shipping channels. For instance, a large-format good (e.g., a desktop) being delivered by a two-person courier may be delivered with a smaller delivery good (e.g., clothes) that was purchased separately but shipped to the same address. Shipping may be a link within the supply chain that directly affects the consumer and triggers their satisfaction. The reliability of goods shipping service is a road to consumer’s IOS. It implies that the consumer can receive the ordered product, that is well packed, which quantity, quality, and specification unit in accordance with the order to a set delivery time and place. According to Ziaullah et al. (2014), a reliable, safe, and timely delivery is something essential for online consumer satisfaction. Shipping service is evaluated by consumers once shopping goods purchased online are shipped quickly, safely, and received on time in accordance with its promise. The most reason why consumers switched to startup companies is attributable to shipping, notably delayed goods received. Contrary, a study by Ahmadinejad et al. (2014) found that shipping sensitivity had no impact on the consumer’s purchase intention. Shipping services ought to be investigated as a result of our understanding of the interaction between shipping and IOS. However, the interaction between shipping and IOS remains unclear. Accordingly, it is therefore hypothesized as follow: Hypothesis 3. There is a positive and meaningful relationship between shipping and ATT Hypothesis 4. There is a positive and meaningful relationship between shipping and IOS 2.3. Attitude The nature of the selling context of consumer orientation and attitude is taken into account to influence consumer CIOS. Today, attitude occupies a central role within the theories and analysis concerning consumer behavior. From a business perspective, consumer attitude is responsible for an evaluation of a product or service. Concerning consumer attitude towards product and service, the previous study claimed that attitude is an important factor in influencing consumers’ intention towards product and service (Chaniotakis et al., 2010). We begin by considering the definition of attitudes, which is individually attributed to emotions, beliefs, and behavioral tendencies an individual has towards a specific object. We have concentrated on uncovering the factors that affect the attitude towards goods purchased online. According to Andrews &amp; Bianchi (2012), consumers’ attitude on online shopping is an important factor that influences the IOS. Through internet shopping, consumers can purchase more alternatives for products and shipping services Attitude is defined as the degree of one’s positive feelings about taking part in online shopping (Azjen, 2005). A purchase will happen solely if consumers exhibit a positive attitude towards online shopping. Chiu et al. (2005) argued that consumers' attitudes, be it positive or negative are related to finishing shopping for behavior via the website. Pavlou &amp; Fygenson (2006) reported that attitude plays a vital role in forming IOS. It is believed that consumers with more positive attitudes can have a lot of tendencies to repeatedly purchase online. This is often indicated by many studies that attitude has a positive impact on the intention to purchase online (Moshrefjavadi et al., 2012; Zhao &amp; Othman, 2011). To analyze consumer's attitudes, we use multi-dimensional models, which consider attitude to be a construct of 3 components: cognitive, affective, and conative (Jin, 2011). This research focus explains model predictions of attitude and IOS. Though most attitudes have all 3 components mentioned above, they will be strongly rooted in either the cognitive or the affective component (Lim et al., 2011). Multi-dimensional models are used to describe the attitude of individual willingness to determine the consumers’ attitudes towards online shopping. Thus, an attitude reflected by indicators such as useful, entertaining, and interesting is used to identify consistencies in attitude as an indicator of consumer goodwill. In this case, the researcher seeks to identify an individual’s needs through the attitude dimension as a moderating variable. Accordingly, we have proposed the following hypothesis: Hypothesis 5. There is a positive and meaningful relationship between ATT and IOS Hypothesis 6. There is a significant effect between SQ and SHP on ATT 2.4. Intention of online shopping The IOS is adopted from several earlier studies that designated continuance intention as a dependent variable (Wang et al., 2012). In a similar, Heijden et al. (2003) define intentions as the consumer’s conscious motivation to make an effort to engage in a specific behaviour. In short, IOS is how hard consumers are willing to try, of how much of an effort they are planning to exert, in order to perform the behaviour (Lindblom &amp; Lindblom 2018). Online shopping is defined as the process a consumer takes to purchase goods over the internet. According to Chaffey (2009), online shopping is buying goods and its related services (included delivery service) over the Internet. The consumers are interested in exploring shopping websites depending on the goods and services being sold, the quality of website services, reliability of promised shipments, the experience of using online shopping websites, and payment security and privacy (Shergill &amp; Chen, 2005). These factors affect customers’ behavior when shopping online. In this research, the concept of intention is analyzed by using the Theory of Planned Behaviour (TPB) by Ajzen (2005). We use TPB because it is suitable to explain any behavior which requires planning, such as consumers' IOS. Various researchers used different approaches to the TPB model: TPB was integrated with the construct(s) derived from various theories or models (Limayem et al., 2007). In another study, TPB was employed as a research of the attitude towards the transaction and subjective norm directly influences the intention of online transactions (Pavlou, &amp; Fygenson, 2006). The TPB is based on three factors, namely, attitude toward the behavior, subjective norms, and perceived behavioral control. As indicated in TPB, attitudes of consumers are affected by subjective norms to deal with IOS. Furthermore, subjective norms are expressed by how the behavior is being affected by the perceived opinions of others. Our study tried to reveal that attitude is positively related to consumers’ CI. It can be argued that TPB is a useful theoretical framework to investigate the attitudes among consumers to engage in consumers’ IOS. TPB proposes that consumers' intentions can best be predicted by their attitudes, subjective norms, and perceived behavioral control (Ajzen, 2005). Under the TPB especially due to the addition of the perceived behavioral control factor, a decision to act is the best predictor of consumer intentions. This study examines consumer IOS using the key constructs of the TPB which are attitude and subjective norms. Taking an attitude-behaviour approach, this study empirically tests relations of consumer personal values, attitude, social norm, perceived behavioral control (PBC), and willingness to buy goods online. Lim et al. (2016) found that a person has a positive attitude, adheres to subjective norms, a span of stimulation of perceived behavioral control and intention, that certain people will perform the behavior. The previous study related to subjective norms focused on online shopping and the subjects of analysis focused on consumers (Limayem et al. 2000). Xie et al. (2011) found that subjective norm has a direct significant influence on the IOS. This was confirmed by Farooq et al. (2019) that the main predictors of consumer IOS are attitude and subjective norms. Trust is a crucial factor in online purchasing intention. Perceived risk has also been a determining factor in Internet shopping intention (Leeraphong &amp; Mardjo, 2013). Hence, trust has a key role in online transactions that can change attitudes and online shopping intentions (Heijden et al., 2003). In an online shopping setting, trust is the willingness of a party to be vulnerable to the actions of another party. Consumers are required to trust in an impersonal provider of goods for an online transaction to occur. In summary, IOS translates to the consumer’s willingness to repurchase goods via the internet. Also, the IOS is defined as a specific desire to continue an online shopping relationship with a goods provider. Accordingly, it is therefore hypothesized as follow: Hypothesis 7. There is a significant influence between SQ, SHP, and ATT towards IOS Hypothesis 8. There is a significant contribution SQ, SHP to IOS when controlled by ATT 3. Method, Data, and Analysis The approach we adopted for building the intention model follows the original exploratory method by Malhotra (2007), which provides eligibility criteria to explain a phenomenon. The research was conducted in the Jakarta metropolitan area using a survey technique. The target population of this research was consumers who intended to purchase goods online. The probabilistic samples were collected using the following criteria: (1) the respondents have had online shopping experiences using e-commerce websites, and the website-based apps and mobile-based apps; and (2) the respondents have shopped online at least three times in the last three months. This criterion relates to the aim of this study, to examine the IOS. This research was carried out for two months, from January to February 2020. The survey method as a data collection tool uses a questionnaire distributed to members of a population. The survey method is used in this study to obtain information regarding the consumers’ CIOS and identify the relationship between two variables or more in certain situations. The survey technique is used to identify and understand the following variables: (1) SQ; (2) shipping; (3) attitude; (4) IOS. Sampling is performed by allotting the same probability of selection to all units of analysis within the population. Based on the sampling, the following sampling measures were applied: First, to accurately determine a target group, samples are selected to represent the target population. Second, identify the elementary units of the population (N), namely, 10,344,018 smartphone users, and then assess the population based on a confidence degree of 95% and standard error of ± 5%. This study applied a margin of error of 5% or 0.05. The sample size used the Slovin’s formula (Almeda et al., 2010) as follows: n = N / (1 + (N x e ²)) (1) n = 10,344,018/ (1 + (10,344,018x 0.05 ²)) n = 10,344,018/ (1 + (10,344,018x 0.0025)) n = 10,344,018/ (1 + 25,860.045) n = 10,344,018 / 25,861.045 n = 399.962 When rounded out, the sample size is 400. n = sample N = population e = margin error The The sample size of 400 respondents was suitable criteria (Hooper et al., 2008). Table 1. Survey Items Variables Indicators SQ adapted from Ahn et al. (2004), Parasuraman et al. (2005), Zhou et al (2019) SQ1 tangible SQ2 reliability SQ3 responsiveness SQ4 assurance SQ5 empathy SHP adapted from Ziaullah et al. (2014) SHP1 reliable SHP2 safe SHP3 affordable SHP4 consolidated SHP5 timely SHP6 return of goods ATT adapted from Ajzen (2005) ATT1 useful ATT2 entertaining ATT3 interesting IOS adapted from Ajzen (2005) IOS1 intend IOS2 willingness IOS3 friendly IOS4 subjective norm IOS5 fruitful IOS6 beliefs Notes: SQ= Service quality, SHP= Shipping, ATT= Attitude, IOS= Intention of online shopping The Pearson correlation coefficient is an important method to measure the similarity of multiple data variables. It returns the values between -1 and 1. When the correlation coefficient equals 1, it becomes a completely positive correlation; when it equals −1, it becomes a completely negative correlation. It means that the greater the absolute value of the correlation coefficient, the stronger the correlation and vice versa (Adler &amp; Parmryd, 2010). (2) Where: N = the number of pairs of scores Σxy = the sum of the products of paired scores Σx = the sum of x scores Σy = the sum of y scores Σx2 = the sum of squared x scores Σy2 = the sum of squared y scores A linear regression line has an equation of the form as follows: Y = a + bX (3) Where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0). 4. Result and Discussion For the demographic section, the respondents are divided into 4 different characteristics which are gender, level of education, age as well as total annual income. In terms of gender, 42% of the respondents are male and 58% are female. In terms of age, the majority of respondents came from the millennial generation and generation Z by 88.9% and only a small portion of the baby boomer generation was 4.2%. In terms of the education of the respondents, most of them had high school education (37.5%), followed by those with a diploma, undergraduate and postgraduate level, respectively 19.5%, 23.8%, and 19.36%. These results indicate that millennials tend to have a better education. In terms of the level of income, 35% of low-income, middle-income, 59%. In turn, 6% of high-income (see Table 2). Table 2. Profile of respondents Characteristics Categories Frequency (%) Gender Male 168 42 Female 232 58 Total 400 100 Education Junior high school 13 3.2 Senior high school 137 34.3 Diploma 78 19.5 Undergraduate 95 23.8 Postgraduate 77 19.3 Total 400 100 Age group 13 - 23 275 68.8 24 - 41 80 20.1 42 - 52 28 6.9 53 and above 17 4.2 Total 400 100 Annual income Low 140 35 Middle 236 59 High 24 6 Total 400 100 Source: primary data The analysis in this section is conducted to provide information about the consumer profiles consist of start-up choices and products purchased online. Based on Table 3, the choice of application/website for online shopping is as follows: Shopee (24.50%), Tokopedia (23.25%), Bukalapak (15.75%), Blibli (14.50%), Lazada (11.50%) and JD.id (10.50%). The results of a comparison between the research report by iPrice (2020) Indonesia for the Q3 of 2020 and the Indonesian E-commerce Map, which ranks the large e-commerce players based on the average website visitors in each quarter, are not much different in succession: Shopee, Tokopedia, Bukalapak, Lazada, Blibli, and JD.id. Table 3. Start-up Options for Online Shopping Start-up Frequency (%) Cumulative (%) Bukalapak 63 15.75 15.75 Blibli 58 14.50 30.25 JD.id 42 10.50 40.75 Lazada 46 11.50 52.25 Shopee 98 24.50 76.75 Tokopedia 93 23.25 100 Source: primary data The categories of products purchased online are fashion products; 32.75%, souvenirs, 14.25%; cosmetic products, 13.50%; gadgets, 13.50%; electronics, 8%; and health products, 18%. Research by MARS Incorporated (2018) shows relatively similar results and indicates that fashion products are the most popular among consumers. The products most often purchased online are fashion (45.8%), accessories (10.9%), and shoes (6.7%). Only health products are different, this is due to the Covid-19 pandemic. The economic pressure due to the Covid-19 pandemic at the time of the survey was not visible. The Covid-19 case in Jakarta has not been released by the government. The results of the confirmatory factor analysis via SEM are used to analyze each indicator variable in this study. Construct measurements are presented in Table 4. Table 4. Summary of measurement scales Variables Indicators Weight value factor α CR P SQ tangible .65 .43 25.70 .00** reliability .90 .82 62.59 .00** responsiveness .78 .61 37.41 .00** assurance .92 .85 70.60 .00** empathy .65 .43 25.70 .00** SHP reliable .75 .56 33.26 .00** safe .70 .49 28.92 .00** affordable .78 .61 37.14 .00** consolidated .68 .46 27.62 .00** timely .86 .75 50.77 .00** return of goods .55 .30 19.43 .00** ATT useful .82 .67 42.08 .00** entertaining .85 .72 47.16 .00** interesting .83 .69 43.80 .00** IOS intend .79 .62 37.81 .00** willingness .79 .63 38.82 .00** friendly .76 .57 34.33 .00** subjective norm .83 .69 44.47 .00** fruitful .83 .69 44.18 .00** beliefs .85 .73 48.75 .00** Notes: SQ= Service quality, SHP= Shipping, ATT= Attitude, IOS= Intention of online shopping The various types of SQ organized by start-up were measured by the SQ, which requires SQ attributes, namely, tangibility, reliability, responsiveness, assurance, and empathy. The results showed that assurance (with α = .85), reliability (with α = .82), and responsiveness (with α = .61) were the main service quality measures, which indicates that start-up companies that already running well as a trusted and reliable start-up company. The construct of SQ observed is consistent with the research by An &amp; Noh (2009); Eid (2011); Flavia’n and Guinaly'u (2006); Zhou et al. (2019), who indicated that the perceived SQ is based on reliability (i.e., accurate, fully responsible and without access failure) and assurance (i.e., security, protect data, and guarantee not to abuse). Hereafter, the constructs of shipping include (1) reliable, (2) safe, (3) affordable, (4) consolidated, (5) timely, and (6) return of goods. The results showed the following: timely, α = .75; affordable, α = .61; and reliable, α = .56. Thus, these three constructs are the main measures of shipping, which indicates that timeliness does affect consumer perception of continuance intention to purchase in online shopping. This result is consistent with the study by Ziaullah et al. (2014), who reported that a reliable, safe, and timely delivery is something essential for online consumers. Several indicators are used to measure attitude, namely, useful as a cognitive element (α = 0.67), entertaining as an affective element (α = 0.72), and interesting as a conative element (α = 0.69). All three indicators are the primary measures for attitude. In addition, the constructs of IOS include intend (α = .62); willingness (α = .63); friendly (α = .57); subjective norms (α = .69), fruitful (α = .69) and beliefs (α = .73), which are the primary measures for the IOS. We used a bivariate Pearson Correlation to test whether there is a statistically significant linear relationship between height and weight, and to determine the strength and direction of the association among the variables tested. Thus, this model can be used as a tool to confirm the theory formed based on observational data. Table 5. Pearson Correlation Confidence Intervals 95% Confidence Intervals (2-tailed)a Pearson Correlation Sig. (2-tailed) Lower Upper SQ - ATT .382 ˂.000 .294 .462 SQ - IOS .332 ˂.000 .241 .416 SHP - ATT .624 ˂.000 .559 .680 SHP - IOS .584 ˂.000 .514 .644 ATT - IOS .718 ˂.000 .666 .762 a Estimation is based on Fisher's r-to-z transformation with bias adjustment. Table 6. Regression Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 SHP, SQb . Enter a. Dependent Variable: ATT b. All requested variables entered. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .629a .395 .392 1.407 a. Predictors: (Constant), SHP, SQ ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 513.743 2 256.872 129.727 .000b Residual 786.097 397 1.980 Total 1299.840 399 a. Dependent Variable: ATT b. Predictors: (Constant), SHP, SQ Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 2.661 .693 3.842 .000 SQ .083 .040 .093 2.057 .040 SHP .317 .025 .577 12.796 .000 a. Dependent Variable: ATT Table 6. Regression Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 ATT, SQ, SHPb . Enter a. Dependent Variable: IOS b. All requested variables entered. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .738a .545 .542 2.151 a. Predictors: (Constant), ATT, SQ, SHP ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 2196.460 3 732.153 158.303 .000b Residual 1831.500 396 4.625 Total 4027.960 399 a. Dependent Variable: IOS b. Predictors: (Constant), ATT, SQ, SHP Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 5.321 1.078 4.935 .000 SQ -.001 .062 -.001 -.020 .984 SHP .216 .045 .223 4.792 .000 ATT 1.019 .077 .579 13.286 .000 a. Dependent Variable: IOS Correlations Control Variables SQ SHP IOS ATT SQ Correlation 1.000 .364 .090 Significance (2-tailed) . .000 .072 df 0 397 397 Bootstrapa Bias .000 -.001 -.002 Std. Error .000 .043 .053 95% Confidence Interval Lower 1.000 .281 -.013 Upper 1.000 .446 .205 SHP Correlation .364 1.000 .250 Significance (2-tailed) .000 . .000 df 397 0 397 Bootstrapa Bias -.001 .000 .000 Std. Error .043 .000 .048 95% Confidence Interval Lower .281 1.000 .157 Upper .446 1.000 .348 IOS Correlation .090 .250 1.000 Significance (2-tailed) .072 .000 . df 397 397 0 Bootstrapa Bias -.002 .000 .000 Std. Error .053 .048 .000 95% Confidence Interval Lower -.013 .157 1.000 Upper .205 .348 1.000 a. Unless otherwise noted, bootstrap results are based on 400 bootstrap samples 5. Conclusion and Suggestion Startup company has the potential to offer micro-, small and medium-sized enterprises almost instant access to the global market like never before. SMEs can trade with a higher number of consumers and partners. In turn, several important enablers play a role in moving the startup ecosystem forward. Looking to the future, technology harnessed to bridge the gap between conventional SQ and electronic SQ. However, technological solutions are less applied by SMEs due to the high costs and fewer involvement of companies. For Indonesia, the e commerce market has expected to grow rapidly over the next five years. The country is home to some 265 million consumers, 171 million of whom are already online. Several major e commerce platforms have increased their interest in the country, although SMEs may not easily adapt to this trend due to the many obstacles met. In general, startups selected by consumers for shopping online are well-established start-ups, which are considered unicorns. Most of the products purchased online, such as fashion, cosmetics, souvenirs or gadgets. We identified certain effects between variables in the structural equation modelling analysis. The findings indicate that SQ and attitude are positively correlated with continuance intention. Besides SQ, attitude constructs are critical factors that promote the IOS. The constructs of attitude, namely, the cognitive, affective and conative elements, can explain each desire and generate a positive attitude towards online purchasing. Finally, this research found that attitude is not a moderator of the relationship between SQ and shipping and IOS. Thus, attitude cannot change the relationship between SQ and shipping with the IOS. Moreover, attitude in the context of e-commerce cannot be used to measure the strength of the relationship between SQ and shipping and the IOS. Implementation of SQ constructs into online shopping that the employed service features produced a significant influence on the IOS. Besides, a startup company must understand who its consumers can differentiate products and services which are offered by the company. This study also has certain limitations. First, this research analyses the effect of attitude towards online shopping, SQ, shipping and IOS. Because there might be some other variables which can affect IOS, the author strongly suggests that in the future research, these other variables should be explored and analysed as well an indicator in this study need to be more widely developed. Second, this research has been conducted exclusively on startup companies that partner with shipping companies and SMEs and the results and conclusions of this research might not apply to other industries. 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