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The Role of Multichannel Integration, Trust and Offline-to-Online Customer Loyalty Towards Repurchase Intention: an Empirical Study in Online-to-Offline (O2O) e-commerce
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Procedia Computer Science 161 (2019) 859–866
The Fifth Information Systems International Conference 2019 The Fifth Information Systems International Conference 2019
The Role of Multichannel Integration, Trust and Offline-to-Online The Role of Multichannel Integration, Trust and Offline-to-Online Customer Loyalty Towards Repurchase Intention: an Empirical Customer Loyalty Towards Repurchase Intention: an Empirical Study in Online-to-Offline (O2O) e-commerce Study in Online-to-Offline (O2O) e-commerce Intan Dewi Savila, Ruhmaya Nida Wathoni, Adhi Setyo Santoso* Intan Dewi Savila, Ruhmaya Nida Wathoni, Adhi Setyo Santoso* President University, Cikarang, Indonesia President University, Cikarang, Indonesia
Abstract Abstract In the current information system literature, the role of trust and customer loyalty towards repurchase intention has been widely In the current information system the role ofregarding trust andthis customer towardsmultichannel repurchase intention has been widely discussed. However, there are still literature, limited discussions topic inloyalty the emerging Online-to-Offilne (O2O) discussed. However, limited this topic in the emerging multichannel Online-to-Offilne (O2O) e-commerce context. there Thus,are thestill purpose of discussions this researchregarding is to examine the influence of multichannel integration and trust towards e-commerce context. Thus,loyalty the purpose of this research is toThis examine the conducts influencequantitative of multichannel integration and trustEquation towards offline loyalty and online to repurchase intention. research method with Structural offline loyalty andbyonline to repurchase intention. This research conducts quantitative method with Structural Equation Modeling (SEM) using loyalty 311 respondent data from O2O e-commerce users in Greater Jakarta Area. The research findings show Modeling (SEM) by using 311 respondent O2O e-commerce Greateronline Jakartaloyalty Area. and Thecustomer research offline findingsloyalty show that both multichannel integration and trust data havefrom significant effect towardusers both in customer that both integration and trustThe have significantof effect bothare customer online loyalty and customer offline loyalty drivemultichannel customer repurchase intention. implications thesetoward findings discussed. that drive customer repurchase intention. The implications of these findings are discussed. © 2019 The Authors. Published by Elsevier B.V. © 2019 2019 The The Authors. Published by B.V. © Authors. by Elsevier Elsevier B.V. This is an open accessPublished article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under CC BY-NC-ND licenseThe (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee Fifth Information Systems International Conference 2019 Peer-review under responsibility of the scientific committee ofofThe Fifth Information Systems International Conference 2019. Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019 Keywords: Multichannel Integration; Trust; Offline Loyalty; Online Loyalty; Repurchase Intention Keywords: Multichannel Integration; Trust; Offline Loyalty; Online Loyalty; Repurchase Intention
1. Introduction 1. Introduction The behavioural changes in shopping behavior in Indonesia are triggered by the growth of Internet users in The behavioural changes shopping behavior in Indonesiain are triggered by the growth Internet users in Indonesia [1]. In recent years,inonline companies (e-commerce) Indonesia experienced a veryofsignificant growth. Indonesia [1]. In recent years, online companies (e-commerce) in Indonesia experienced a very significant growth. The total number of e-commerce users in Indonesia in 2016 reached 26.2 million [2]. One of the rapid growth eThe total number of e-commerce users in Indonesia in 2016 reached 26.2 million [2]. One of the rapid growth e-
* Corresponding author. Tel.: +62-21-891-097-62. address:author. [email protected] * E-mail Corresponding Tel.: +62-21-891-097-62. E-mail address: [email protected] 1877-0509 © 2019 The Authors. Published by Elsevier B.V. 1877-0509 © 2019 Thearticle Authors. Published by Elsevier B.V. This is an open access under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019 This is an open access article under CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019 1877-0509 © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019. 10.1016/j.procs.2019.11.193
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commerce platforms in Indonesia is Berrybenka, an online leading fashion and beauty shopping site [3]. Berrybenka sells more than 1000 and international brands, including in-house label products. Fun, easy, and reliable online shopping experience to satisfy customers with newest product collection and special offers in daily was Berrybenka’s commitment. Berrybenka also gives another benefit including product return policy up to 30 days after the customers receive the item. It also has cash on delivery methods to make the payment easier for those who are not comfortable with card payment in online shopping. Berrybenka also provides free shipping services for delivering the products [3]. The fashion e-commerce landscape is widely known in keep providing promotion and discount pricing. In this situation, Berrybenka compete with other strong player in fashion e-commerce for providing the best service to get their customer loyalty and trust. In order to face this challenge, Berrybenka implement multichannel Online-to-Offline (O2O) e-commerce strategy. In information system literature, O2O e-commerce can be interpreted as the crosschannel integration service between online and offline channel that enable the customer to perform shopping activities through both channels sequentially [4]. This integration of has potential to provide the advantage to get customer loyalty and trust through both online and offline channel [5]. While there are many discussions regarding the antecedents and consequences of customer trust and loyalty for ecommerce, however, the discussion about this topic in O2O e-commerce context is still limited. Therefore, the specific objective of this research is to examine the antecedents of customer repurchase intention that related with customer loyalty in O2O e-commerce context. In order to achieve the research objective, this research raises a research question; whether the multichannel integration and trust toward the O2O e-commerce brand influence both offline and online customer loyalty that drives customer repurchase intention. 2. Literature
review
2.1. Multichannel integration Multichannel integration is characterized as the management of diverse channels that offer customers a consistent experience over the majority of the company's product or services [6]. In previous literature, multichannel integration strategy represents the effort for increasing both online and offline loyalty by offering integrated various transaction channel including purchasing from a store, purchasing from a website, phone ordering, mail orders, and comparisonshopping site [7]. Beside as cross-transaction channel, multichannel integration also has role as cross media promotions channel to obtain and maintain a loyal customer that have been obtained by utilizing various forms of media communication or provide some discount and promotion that can be implemented within cross-channel [6]. 2.2. Trust Trust is essential for the success of e-commerce activities [8]. In marketing the product, the seller must be able to give a sense of confidence to prospective buyers in order to avoid deception. In the previous literature, it is found that experience, environment, friends, insecurity, and disbelief of others are one factor that causes a person to trust or not the seller [9]. Other researchers, [10] have found that consumers are willing to try brand extensions when the brands are highly trusted, at which times brand trust compensates for the lack of knowledge about the new products. In the context of multi-channel retailing, the consumer’s trust in the retail brand may well influence his or her acceptance of the retailer’s offerings at a new business channel such as the Internet. 2.3. Offline loyalty Offline Loyalty has a significant consideration on its part with the location. It also can be called as customers who prefer to buy product in physical store, instead of buying in online. The customer decides to repurchase if they feel comfortable with the loyalty program provided by the store and the retailer must try to find ways to keep their loyal customers [11]. Customers who prefer to buy product in physical store instead of buying in online can be defined as offline loyalty. The customer decides to repurchase if they feel comfortable with the loyalty program provided by the store and of course the retailer must try to find ways to keep their loyal customers.
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2.4. Online loyalty The study of [12] confirms the synergies between channels since they find that the frequency of in-store purchasing is related to the frequency of online purchasing and that the frequency of online searching is related to the frequency of in-store purchasing. The most significant reasons to people for buying in online are easier and another reason was discount prices. 2.5. Repurchase intention Consumers decide to purchase again in a different product [13]. Person who has repeating purchase and also decision to engage with the same e-commerce site in the near future is repurchasing intention [14]. Repurchase intention behaviour tends to be more favourable than purchase intention, because it indicates continue to purchase in the future [15]. Most of researchers argue that repurchase intention one of the most appropriate dependent variables in any system of relationships designed to develop management insight and improved strategic planning and service delivery also have looked at the influence of satisfaction on loyalty and varying components of behavioral intention [16]. Other researcher stated trustworthy in e-commerce site from the internet was affected by the consumers repurchase intention. Lee et al. [17] and Meskaran et al. [18] describe a situation where a consumer is willing and intends to make an online transaction is called as online purchase intention. 2.6. Research gap On previous study that researcher used for the reference for this study had almost the same variables, which are multichannel integration towards offline loyalty and online loyalty but it focused on retail apparel sector of Spain and the United Kingdom [4]. Another previous study stated similar relationship between trust to the offline loyalty and online loyalty in case of cellular service operators like Mobilink, Telenor, Warld, Ufone and Zong. In some resources, Sarwar et al. [19] and Haslinda et al. [20] mentioned that it has similar relationship in trust towards customer loyalty of liberalization of the Malaysian banking industry. Another researcher has measuring loyalty in online and offline towards repurchase intention in case of hotel customers in hospitality sales and marketing [21]. Meanwhile, this research was conduct about the relationship between multichannel integration towards offline loyalty, multichannel integration towards online loyalty, trust towards offline loyalty, trust towards online loyalty, offline loyalty towards repurchase intention and online loyalty towards repurchase intention which focused in Berrybenka, retail apparel sector of Indonesia. Due to some gaps between this study and the previous study, the author would like to look further and do this research by applying the theories from the previous research. 3. Research method 3.1. Theoretical framework In this research studies about the factors that affect the online to offline strategy in the retail store, focusing on fashion e-commerce. Investigation about online to offline strategy has to include multichannel integration and trust by the customers to assess the measurement. Both of these variables may affect the offline loyalty and online loyalty of buying at the investigated fashion e-commerce, resulting in the repurchase intention. 3.2. Hypothesis To examine does multichannel integration affecting customer loyalty in O2O companies, this research conducted 6 hypotheses. As follows:
H1. Multichannel Integration has a significant effect to Offline Loyalty
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H2. Multichannel Integration has a significant effect to Online Loyalty
To get loyal customer in online and offline store it must have the best deals, a consistent quality of experience, have a good relationship with the customer for making it as a successful multichannel strategy [22]. In online and offline channels and the shopping experience can be increased if a retailer succeeds in integrating on the channel itself. When channel has been integrated, customers less likely to switch to the other company based on [5] proved on overall loyalty, there are positive direct effects of channel integration. Also, if loyal customers have a bad experience and dissatisfied with the current service, then they might be moved to another company.
H3. Trust has a significant effect to Online Loyalty
H4. Trust has a significant effect to Offline Loyalty
Trust is depending on beliefs built and current attitude with the offline stores as well as the emergent expectations based on the online operation, it is valid for a multichannel retailer. The literature on the relational outcomes of trust is overflow as long if it still in line with the important role of trust in building and maintain relationships. Based on [5] confirmed that the relationship between trust in the retailer and loyalty is on several things.
H5. Offline Loyalty has a significant effect to Repurchase Intention
H6. Online Loyalty has a significant effect to Repurchase Intention
Repurchase become one of the important things in e-commerce. Recent researches show that repurchase intention represent a customer’s buying behavior in further repurchase behavior. Repurchase intention bring to customer returns for buying again and again; even it is in a short or long term. Some of researcher stated that repurchase intention has a significant dominant to customer loyalty. Online and offline loyalty can be called as repurchase intention as long as it is a consequence and it led to increase in company’s profitability. 3.3. Instruments In order to obtain the data which are valid and reliable, this study using the online questionnaire as an instrument to collect all the data of the survey. The questionnaire is easy to use in analyzing the data, especially for the quantitative research where the data is in the formed of number [23]. The construct measurement used for this research study is selected and adapted from previous studies, which are multichannel integration, trust, offline loyalty, online loyalty and repurchase intention presented in Table 1. The research using the likert scale technique, where positioning number 1 until 6 as strongly disagree to strongly agree. Table 1. Measurement Indicators. Construct
Measurement Indicators
Adapted Source
Multichannel
It is convenient to return goods I have bought online to any of Berrybenka’s physical store
[5, 24]
Integration
Berrybenka physical store allows me to do an order online At Berrybenka’s website it is easy to get information on order and delivery status also for products ordered offline At Berrybenka’s website it is easy to get real-time information on product availability It is easy to search for store locations and opening hours at Berrybenka website Berrybenka sells online the same products as in the physical stores Berrybenka offers the same prices online as in the physical stores Berrybenka offers the same promotions online as in the physical stores
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Construct
Trust
863 5
Measurement Indicators
Adapted Source
Shopping over the online website would be a safe way to shop
[5, 25]
Shopping over the online website would be very risky I would trust online shopping in online website I am prepared to give private information to online companies It is not a problem to pay in advance for purchased products over the internet I follow the brand in all of its activities
Offline Loyalty
I will continue visiting Berrybenka physical store more often to me, it is the best company to do business with
[5, 26]
As long as the present service continues, I doubt that I would switch to another company When I need to use similar service that this company offers, it will be my first choice On this website I can use my loyalty card or redeem coupons obtained offline
Online
On this website I can obtain online coupons to be used offline
Loyalty
When I need to make a purchase, this website is my first choice
[5, 25]
I consider myself to be a loyal patron of the online website I would recommend this company’s website to others I am willing to use Berrybenka when online shopping Repurchase
I plan to use Berrybenka when online shopping
Intention
It is likely that I will repurchase from this Internet store in the near future
[10, 27]
3.4. Sampling This study is measured based on the customer experience during purchasing activity through e-commerce [28]. On the population of this study, where the number and character of the respondent are unknown clearly and this study using non-probability sampling which is purposive sampling technique [29]. The purposive sampling technique is a type of non-probability sampling technique where the researcher chooses the criteria of respondent based on their own criteria that appropriate with the research objective To fully investigate the possible factors affecting online to offline strategy in Berrybenka, a questionnaire form consists of respondent profile and 25 measurement items was created using online survey through Google Form. The survey was delivered mainly through social media by sharing the questionnaire link. The targeted respondents were people who have bought in Berrybenka. The requirement of the respondent was characterized by gender, age, buying experience, and the average transaction in Berrybenka. The period of collecting filled questionnaires took about 2 weeks. From 2153 contacts delivered the questionnaire, 347 provide the response (16, 12% response rate). The amount of valid questionnaire to use for the investigation is 311 and none of the questionnaire is incomplete. 3.5. Data analysis method Factor analysis is used to determine the validity of the construct. Several evaluations are conducted in the measurement, such as KMO, Bartlett test, anti-image, principle component analysis, communalities, total variance explained and rotated component matrix. The validity test is measured to evaluate whether the questionnaire result is representative the sample or not [30]. The data result provided has to be qualified in several criteria of the measurement. In order for the data to be considered valid, The KMO of the data has to above 0.5, the Bartlett has to under 0.5, the anti-image has to be above 0.5, the principle component analysis has to be above 0.5, the communalities has to be above 0.5, the total of variance explained has to be above 60%, and lastly, the rotated component matrix used for the amount of respondents above 200 is 0.4. After the test conduction, it was found that multichannel integration; trust, offline loyalty, online loyalty, and repurchase intention are indeed valid for the measurement. All
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of the valid items should be tested for their reliability afterwards. The qualification for reliability test is the Cronbach’s Alpha has to be greater than 0.6 as stated from [31]. 4. Result and discussion
Fig, 1. Research Framework (with p-value for Hypothesis testing).
SPSS AMOS 23rd version is used for data analysis. In order to test hypotheses about relationship between variables SEM is used (see Fig. 1). First of all, the drawing has to be made and then begin with the analysis. We used Model fit as a comparison between the requirement of data, and the result of the analysis. Based on the result, the model fit the data. The Goodness-of-Fit is measured to determine whether a variable can be accepted or rejected. The seven indicators of Model Fit were used in this study. The cut of value is the criteria of each category. CMIN/DF as a result of value is 3,543, which it was a good fit because it is less than 5. GFI should be more than 0,5 and less than 0,9 but it showed 0,916 so it is poor fit. AGFI had more than 0,5 and less than 0,10 in the results 0,861 so it is marginal fit. Then, IFI criteria, TLI criteria, CFI criteria should be more than 0,9 and the results shows that IFI value showed 0.949, TLI had 0.927 and CFI was 0.948 so it was a good fit. Last, RMSEA’s criteria should be 0.05 ≤ and ≤ 0.08, and the result shown is 0,091 which mean it doesn’t suit with the criteria. All of the model-fit tells exceeded the common acceptance levels proposed by recent research, showing that the structural model displayed a good model fit. Based on our research, we found these data result, our KMO data is 0.900, it is above 0.5 that become a threshold for construct validity. The bartlett data is 0.000, it below 0.5, the communalities should be more than 0,4, and it is more than 0,5 with range 0.734 to 0.917, our total variance explained data is 81.892%, it is above 60%. For rotated component matrix, it should be used 0,4 if more than 200 respondents and some of the component has been eliminated because it didn’t match with the criteria and it show a discriminant issue which causes the removal of some variables, they are Multichannel Integration (1, 2, 3, 4, 5), Trust (3), Offline Loyalty (3), Online Loyalty (3,4,5) and none for Repurchase Intention’s statements. The first variable is multichannel integration with value range from 0.804 to 0.845, then there is trust with value range 0.682 to 0.773, offline loyalty with value range 0.611 to 0.809 and online loyalty with value range 0.864 to 0.890, the last variable is repurchase intention with value range 0.471 to 0.815. The Cronbach’s should be more than 0.6 to ensure the construct reliability. The Cronbach’s alpha data of multichannel integration is 0.877, the Cronbach’s alpha data of trust is 0.812, the Cronbach’s alpha data of offline loyalty is 0.848, the Cronbach’s alpha data of online loyalty is 0.872, and the Cronbach’s alpha data of repurchase intention is 0.927. Table 2. Regression Result. Hypothesis
Estimate
S.E.
C.R.
p-value
Multichannel Integration Offline Loyalty
-1.146
0.347
-3.303
0.000
Multichannel Integration Online Loyalty
-0.830
0.219
-3.790
0.000
Trust Offline Loyalty
2.992
0.587
5.098
0.000
Trust Online Loyalty
2.199
0.354
6.371
0.000
Intan Dewi Savila et al. / Procedia Computer Science 161 (2019) 859–866 Author name / Procedia Computer Science 00 (2019) 000–000
Hypothesis
Estimate
S.E.
C.R.
p-value
Offline Loyalty Repurchase Intention
1.166
0.126
9.264
0.000
Online Loyalty Repurchase Intention
-0.175
0.80
-2.188
0.029
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According to the appendix Table 2, the P-value is should be less than 0,05 and then the null hypotheses will be accepted, if it’s have more than 0,05 then it could be rejected. The estimates value tells the relations between variables, and the estimates value for Multichannel Integration to Offline Loyalty is -1.146 which means when the value of Multichannel Integration is 1 then the value for Offline Loyalty will be increased 1.146. For the Critical Ration, it is the measurement of parameter estimate divided by an estimate of its standard error. It showed that the strongest correlation of C.R is Trust towards Online Loyalty. It shows how strong the connection between variables, while the sign demonstrates the correlation direction whether it is positive or negative correlation. The higher value of C.R the stronger the correlation will be. The strongest relationship has showed in the Estimate value, when the Estimate value below (d) 0.5, it can assume as weak relationship. As can be seen on the appendix, there is one model that has higher relationships, which is Trust towards Offline Loyalty (0,621). After analyse the data, the result showed that 6 hypotheses are supported as describe above (p<0.05), there are multichannel integration influences offline loyalty, multichannel integration influences online loyalty, trust influences offline loyalty, trust influences online loyalty, offline loyalty influences repurchase intention lastly online loyalty influences repurchase intention [24]. Conducted a research and indicates it result that loyalty towards the offline channel of the multichannel retailer is positively related to loyalty towards its online channel. Another result also found that multichannel integration is positively related to customer loyalty towards the offline and online channel of the retailer. 5. Conclusion and recommendations The results of this research which concern about the influence of multichannel integration, trust and customer loyalty towards repurchase intention an empirical study in Berrybenka shows that; 1) multichannel integration has a significant influence to offline loyalty; 2) multichannel integration has a significant influence to online loyalty; 3) trust has a significant influence to online loyalty; 4) trust has s significant influence to offline loyalty; 5) offline loyalty has a significant influence to repurchase intention; 6) online loyalty has a significant influence to repurchase intention. Nowadays, the competition between fashion retail industries is quite tight. For company, they should do an improvement for their performance. Especially on their online loyalty, because in this research shown that most of customer who already purchased in Berrybenka was preferring to buy in offline. Berrybenka was based online-fashion company, so if their online loyalty didn’t have a significant impact to their customer, how can they compete with the other competitor? Therefore, they can develop another strategy to achieve more market share. Limitations of this research are as follows: The sample of this research only limit to Berrybenka’s online and offline customer who already purchased in Berrybenka and the factors only limits to several factors in O2O strategy. Based on these findings that explore the consequences of multichannel integration, further research may explore the antecedents of multichannel integration that still has limited discussion in information system literature [6, 4, 24]. References [1] [2] [3] [4] [5]
Fajriah, L. R. (2017) A Survey on Online Shopping Rituals during Ramadhan. Available from: https://autotekno.sindonews.com/read/1217460/133/survei-belanja-online-jadi-pilihan-utama-masyarakat-selama-ramadhan-1499010507. [Accessed 2nd July]. Septian, D. (2016) E-commerce in Indonesia Reached 26.2 Million. Available from: http://bisnis.liputan6.com/read/2602680/bps-jumlah-ecommerce-di-indonesia-capai-262-juta. [Accessed 15th September]. Berrybenka. (n. d). (2018) Available from: http://www.berrybenka.com. [Accessed 1st September]. Frasquet, Marta, and Maria-José Miquel. (2017) “Do Channel Integration Efforts Pay-Off in terms of Online and Offline Customer Loyalty?” International Journal of Retail & Distribution Management 45 (7/8): 859-873. Frasquet, Marta, Alejandro Molla Descals, and Maria Eugenia Ruiz-Molina. (2017) “Understanding Loyalty in Multichannel Retailing: The Role of Brand Trsut and Brand Attachment.” International Journal of Retail & Distribution Management 45 (6): 608-625.
866 8
[6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31]
Intan Dewi Savila et al. / Procedia Computer Science 161 (2019) 859–866 Author name / Procedia Computer Science 00 (2019) 000–000
Chatterjee, Patrali. (2012) “Multiple-Channel and Cross-Channel Shopping Behavior: Role of Consumer Shopping Orientations.” Journal of Service Research 28 (1): 9-24. Linton, I. (n.d.). “the Definition of Multi-Channel Retailing.” Available from: http://smallbusiness.chron.com/definition-multichannelretailing-20263.html. Crowell, William. (2012) “Trust, The E-Commerce Difference.” Credit Card Management, 14 (5): 80. Handika. (2015) Trust on Online Transactions. Available from: https://www.kompasiana.com/handika_058/kepercayaan-jual-belionline_5619e93f359373a2048b4574. [Accessed 11st October]. Reast, Jon D. (2012) “Brand Trust and Brand Extension Acceptance the Relationship.” Journal of Product & Brand Management, 14 (1): 4-13. RewardPort. (n.d). “Difference Between Online and Offline Loyalty Programs.” Available from: https://www.rewardport.in/blog/2017/09/01/difference-between-online-and-offline-loyalty-programs/. Farag, S., T. Schwanena, M. Dijsta, and J. Faberb. (2007) “Shopping Online and/Or In-Store? A Structural Equation Model of the Relationships Between E-Shopping And In-Store Shopping.” Transportations Research Part A: Policy and Practice 41(2): 125-141. Hellier, K. Phillip, M. Gus Geursen, A. Rodney Carr, and A. John Rickard. (2003) "Customer Repurchase Intention: A General Structural Equation Model." European Journal of Marketing 37 (11/12): 1762-1800. Bayrakter, Erkan, Ekrem Tatoglu, Ali Turkyilmaz, Dursun Delen, and Selim Zaim. (2012) “Measuring he Efficiency of Customer Satisfaction and Loyalty for Mobile Phone Brands with DEA.” Expert Systems with Applications 39: 99-106. Zeithaml, A. Valarie, L. Leonard Berry, and Ananthanarayanan Parasuraman. (2013) “The Behavioral Consequences of Service Quality.” Jorunal of Marketing 60: 31-46. White, Christopher, and Yi-Ting Yu. (2014) “Satisfaction Emotions and Consumer Behavioral Intentions.” Journal of Services Marketing 19 (6): 411-420. Lee, Wan-I., Shan-Yin Cheng, and Yu-Ta Shih. (2017) “Effects Among Product Attributes, Involvement, Word-Of-Mouth, and Purchase Intention In Online Shopping.” Asia Pasific Management Review 22 (4): 223-229. Meskaran, Fatemeh, Zuraini Ismail, and Bharani Shanmugam. (2013) “Online Purchase Intention: Effect of Trust and Security Perception.” Journal Consumer Behavior 7 (6): 307-315. Sarwar, Muhammad Zaman, Kashif Shafique Abbasi, and Saleem Pervaiz. (2012) “The Effect of Customer Trust on Customer Loyalty and Customer Retention: A Moderating Role of Cause Related Marketing.” Global Journal of Management and Business Research, 12 (6): 2736. Haslinda, H., R. A. Teo Poh Kiong, and Ainuddin. (2014) “Effects of Perceived Value and Trust on Customer Loyalty towards Foreign Banks in Sabah, Malaysia.” Global Journal of Emerging Trends in e-Business 1 (2): 137-153. Shahrokh, Zohreh Dehdashti, Nushin Oveisi, and Seyyed Mahdi Timasi. (2013) “The Effects of Customer Loyalty on Repurchase Intention in B2C E-commerce- A Customer Loyalty Perspective.” Journal Basic and Applied Scientific Research 3 (6): 636-644. Storania. (2014) The Concept of Multichannel Marketing. Available from: https://www.storania.com/pengertian-multichannel-marketing/. [Accessed 27th October]. Rowley, Jenny. (2014) “Designing and Using Research Questionnaires.” Management Research Review 37 (3): 308-330. Frasquet, Marta, and Maria-José Miquel. (2017) “Do Channel Integration Efforts Pay-Off in Terms of Online and Offline Customer Loyalty?” International Journal of Retail & Distribution Management, 45 (7/8): 859-873. Ribbink, Dina., C.R. Allard, Van Riel, Veronica Liljander, and Sandra Streukens. (2004) “Comfort Your Online Customer: Quality, Trust And Loyalty on The Internet.” Managing Service Quality: An International Journal, 14 (6): 446-456. Tsiotsou, and H. Rodoula. (2016) “The Social Aspects of Consumption as Predictors of Consumer Loyalty: Online VS Offline Services.” Journal of Service Management 27 (2): 91-116. Mao, Zhenxing, and Jiaying Lyu. (2017) “Why travelers use Airbnb again? An integrative approach to understanding travelers’ repurchase intention.” International Journal of Contemporary Hospitality Management, 29(9): 2464-2482. Etikan, Ilker, Sulaiman Abubakar Musa, and Rukayya Sunusi Alkassim. (2016) “Comparison Convenience Sampling and Purposive Sampling.” American Journal of Theoritical and Applied Statistics 5 (1): 1-4. Acharya, S. Anita, Anupam Prakash, Pikee Saxena, and Aruna Nigam. (2013) “Sampling: Why and How Of It?” Indian Journal of Medical Specialities 4 (2): 3-7. Bai, Yuling, Cong Li, and Jishun Niu. (2016) “Study on Customer-Perceived Value of Online Clothing Brands.” American Journal of Industrial and Business Management 6 (8): 914-921. Al-Jabaly, M. Sameer, and S. Aymen Khraim. (2014) “The Effect of Perceived Value and Customer Satisfaction on Perceived Price Fairness of Airline Travelers in Jordan.” Universal Journal of Management 2 (5): 186-196.
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Procedia Computer Science 161 (2019) 859–866
The Fifth Information Systems International Conference 2019 The Fifth Information Systems International Conference 2019
The Role of Multichannel Integration, Trust and Offline-to-Online The Role of Multichannel Integration, Trust and Offline-to-Online Customer Loyalty Towards Repurchase Intention: an Empirical Customer Loyalty Towards Repurchase Intention: an Empirical Study in Online-to-Offline (O2O) e-commerce Study in Online-to-Offline (O2O) e-commerce Intan Dewi Savila, Ruhmaya Nida Wathoni, Adhi Setyo Santoso* Intan Dewi Savila, Ruhmaya Nida Wathoni, Adhi Setyo Santoso* President University, Cikarang, Indonesia President University, Cikarang, Indonesia
Abstract Abstract In the current information system literature, the role of trust and customer loyalty towards repurchase intention has been widely In the current information system the role ofregarding trust andthis customer towardsmultichannel repurchase intention has been widely discussed. However, there are still literature, limited discussions topic inloyalty the emerging Online-to-Offilne (O2O) discussed. However, limited this topic in the emerging multichannel Online-to-Offilne (O2O) e-commerce context. there Thus,are thestill purpose of discussions this researchregarding is to examine the influence of multichannel integration and trust towards e-commerce context. Thus,loyalty the purpose of this research is toThis examine the conducts influencequantitative of multichannel integration and trustEquation towards offline loyalty and online to repurchase intention. research method with Structural offline loyalty andbyonline to repurchase intention. This research conducts quantitative method with Structural Equation Modeling (SEM) using loyalty 311 respondent data from O2O e-commerce users in Greater Jakarta Area. The research findings show Modeling (SEM) by using 311 respondent O2O e-commerce Greateronline Jakartaloyalty Area. and Thecustomer research offline findingsloyalty show that both multichannel integration and trust data havefrom significant effect towardusers both in customer that both integration and trustThe have significantof effect bothare customer online loyalty and customer offline loyalty drivemultichannel customer repurchase intention. implications thesetoward findings discussed. that drive customer repurchase intention. The implications of these findings are discussed. © 2019 The Authors. Published by Elsevier B.V. © 2019 2019 The The Authors. Published by B.V. © Authors. by Elsevier Elsevier B.V. This is an open accessPublished article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under CC BY-NC-ND licenseThe (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee Fifth Information Systems International Conference 2019 Peer-review under responsibility of the scientific committee ofofThe Fifth Information Systems International Conference 2019. Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019 Keywords: Multichannel Integration; Trust; Offline Loyalty; Online Loyalty; Repurchase Intention Keywords: Multichannel Integration; Trust; Offline Loyalty; Online Loyalty; Repurchase Intention
1. Introduction 1. Introduction The behavioural changes in shopping behavior in Indonesia are triggered by the growth of Internet users in The behavioural changes shopping behavior in Indonesiain are triggered by the growth Internet users in Indonesia [1]. In recent years,inonline companies (e-commerce) Indonesia experienced a veryofsignificant growth. Indonesia [1]. In recent years, online companies (e-commerce) in Indonesia experienced a very significant growth. The total number of e-commerce users in Indonesia in 2016 reached 26.2 million [2]. One of the rapid growth eThe total number of e-commerce users in Indonesia in 2016 reached 26.2 million [2]. One of the rapid growth e-
* Corresponding author. Tel.: +62-21-891-097-62. address:author. [email protected] * E-mail Corresponding Tel.: +62-21-891-097-62. E-mail address: [email protected] 1877-0509 © 2019 The Authors. Published by Elsevier B.V. 1877-0509 © 2019 Thearticle Authors. Published by Elsevier B.V. This is an open access under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019 This is an open access article under CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019 1877-0509 © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019. 10.1016/j.procs.2019.11.193
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commerce platforms in Indonesia is Berrybenka, an online leading fashion and beauty shopping site [3]. Berrybenka sells more than 1000 and international brands, including in-house label products. Fun, easy, and reliable online shopping experience to satisfy customers with newest product collection and special offers in daily was Berrybenka’s commitment. Berrybenka also gives another benefit including product return policy up to 30 days after the customers receive the item. It also has cash on delivery methods to make the payment easier for those who are not comfortable with card payment in online shopping. Berrybenka also provides free shipping services for delivering the products [3]. The fashion e-commerce landscape is widely known in keep providing promotion and discount pricing. In this situation, Berrybenka compete with other strong player in fashion e-commerce for providing the best service to get their customer loyalty and trust. In order to face this challenge, Berrybenka implement multichannel Online-to-Offline (O2O) e-commerce strategy. In information system literature, O2O e-commerce can be interpreted as the crosschannel integration service between online and offline channel that enable the customer to perform shopping activities through both channels sequentially [4]. This integration of has potential to provide the advantage to get customer loyalty and trust through both online and offline channel [5]. While there are many discussions regarding the antecedents and consequences of customer trust and loyalty for ecommerce, however, the discussion about this topic in O2O e-commerce context is still limited. Therefore, the specific objective of this research is to examine the antecedents of customer repurchase intention that related with customer loyalty in O2O e-commerce context. In order to achieve the research objective, this research raises a research question; whether the multichannel integration and trust toward the O2O e-commerce brand influence both offline and online customer loyalty that drives customer repurchase intention. 2. Literature
review
2.1. Multichannel integration Multichannel integration is characterized as the management of diverse channels that offer customers a consistent experience over the majority of the company's product or services [6]. In previous literature, multichannel integration strategy represents the effort for increasing both online and offline loyalty by offering integrated various transaction channel including purchasing from a store, purchasing from a website, phone ordering, mail orders, and comparisonshopping site [7]. Beside as cross-transaction channel, multichannel integration also has role as cross media promotions channel to obtain and maintain a loyal customer that have been obtained by utilizing various forms of media communication or provide some discount and promotion that can be implemented within cross-channel [6]. 2.2. Trust Trust is essential for the success of e-commerce activities [8]. In marketing the product, the seller must be able to give a sense of confidence to prospective buyers in order to avoid deception. In the previous literature, it is found that experience, environment, friends, insecurity, and disbelief of others are one factor that causes a person to trust or not the seller [9]. Other researchers, [10] have found that consumers are willing to try brand extensions when the brands are highly trusted, at which times brand trust compensates for the lack of knowledge about the new products. In the context of multi-channel retailing, the consumer’s trust in the retail brand may well influence his or her acceptance of the retailer’s offerings at a new business channel such as the Internet. 2.3. Offline loyalty Offline Loyalty has a significant consideration on its part with the location. It also can be called as customers who prefer to buy product in physical store, instead of buying in online. The customer decides to repurchase if they feel comfortable with the loyalty program provided by the store and the retailer must try to find ways to keep their loyal customers [11]. Customers who prefer to buy product in physical store instead of buying in online can be defined as offline loyalty. The customer decides to repurchase if they feel comfortable with the loyalty program provided by the store and of course the retailer must try to find ways to keep their loyal customers.
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2.4. Online loyalty The study of [12] confirms the synergies between channels since they find that the frequency of in-store purchasing is related to the frequency of online purchasing and that the frequency of online searching is related to the frequency of in-store purchasing. The most significant reasons to people for buying in online are easier and another reason was discount prices. 2.5. Repurchase intention Consumers decide to purchase again in a different product [13]. Person who has repeating purchase and also decision to engage with the same e-commerce site in the near future is repurchasing intention [14]. Repurchase intention behaviour tends to be more favourable than purchase intention, because it indicates continue to purchase in the future [15]. Most of researchers argue that repurchase intention one of the most appropriate dependent variables in any system of relationships designed to develop management insight and improved strategic planning and service delivery also have looked at the influence of satisfaction on loyalty and varying components of behavioral intention [16]. Other researcher stated trustworthy in e-commerce site from the internet was affected by the consumers repurchase intention. Lee et al. [17] and Meskaran et al. [18] describe a situation where a consumer is willing and intends to make an online transaction is called as online purchase intention. 2.6. Research gap On previous study that researcher used for the reference for this study had almost the same variables, which are multichannel integration towards offline loyalty and online loyalty but it focused on retail apparel sector of Spain and the United Kingdom [4]. Another previous study stated similar relationship between trust to the offline loyalty and online loyalty in case of cellular service operators like Mobilink, Telenor, Warld, Ufone and Zong. In some resources, Sarwar et al. [19] and Haslinda et al. [20] mentioned that it has similar relationship in trust towards customer loyalty of liberalization of the Malaysian banking industry. Another researcher has measuring loyalty in online and offline towards repurchase intention in case of hotel customers in hospitality sales and marketing [21]. Meanwhile, this research was conduct about the relationship between multichannel integration towards offline loyalty, multichannel integration towards online loyalty, trust towards offline loyalty, trust towards online loyalty, offline loyalty towards repurchase intention and online loyalty towards repurchase intention which focused in Berrybenka, retail apparel sector of Indonesia. Due to some gaps between this study and the previous study, the author would like to look further and do this research by applying the theories from the previous research. 3. Research method 3.1. Theoretical framework In this research studies about the factors that affect the online to offline strategy in the retail store, focusing on fashion e-commerce. Investigation about online to offline strategy has to include multichannel integration and trust by the customers to assess the measurement. Both of these variables may affect the offline loyalty and online loyalty of buying at the investigated fashion e-commerce, resulting in the repurchase intention. 3.2. Hypothesis To examine does multichannel integration affecting customer loyalty in O2O companies, this research conducted 6 hypotheses. As follows:
H1. Multichannel Integration has a significant effect to Offline Loyalty
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H2. Multichannel Integration has a significant effect to Online Loyalty
To get loyal customer in online and offline store it must have the best deals, a consistent quality of experience, have a good relationship with the customer for making it as a successful multichannel strategy [22]. In online and offline channels and the shopping experience can be increased if a retailer succeeds in integrating on the channel itself. When channel has been integrated, customers less likely to switch to the other company based on [5] proved on overall loyalty, there are positive direct effects of channel integration. Also, if loyal customers have a bad experience and dissatisfied with the current service, then they might be moved to another company.
H3. Trust has a significant effect to Online Loyalty
H4. Trust has a significant effect to Offline Loyalty
Trust is depending on beliefs built and current attitude with the offline stores as well as the emergent expectations based on the online operation, it is valid for a multichannel retailer. The literature on the relational outcomes of trust is overflow as long if it still in line with the important role of trust in building and maintain relationships. Based on [5] confirmed that the relationship between trust in the retailer and loyalty is on several things.
H5. Offline Loyalty has a significant effect to Repurchase Intention
H6. Online Loyalty has a significant effect to Repurchase Intention
Repurchase become one of the important things in e-commerce. Recent researches show that repurchase intention represent a customer’s buying behavior in further repurchase behavior. Repurchase intention bring to customer returns for buying again and again; even it is in a short or long term. Some of researcher stated that repurchase intention has a significant dominant to customer loyalty. Online and offline loyalty can be called as repurchase intention as long as it is a consequence and it led to increase in company’s profitability. 3.3. Instruments In order to obtain the data which are valid and reliable, this study using the online questionnaire as an instrument to collect all the data of the survey. The questionnaire is easy to use in analyzing the data, especially for the quantitative research where the data is in the formed of number [23]. The construct measurement used for this research study is selected and adapted from previous studies, which are multichannel integration, trust, offline loyalty, online loyalty and repurchase intention presented in Table 1. The research using the likert scale technique, where positioning number 1 until 6 as strongly disagree to strongly agree. Table 1. Measurement Indicators. Construct
Measurement Indicators
Adapted Source
Multichannel
It is convenient to return goods I have bought online to any of Berrybenka’s physical store
[5, 24]
Integration
Berrybenka physical store allows me to do an order online At Berrybenka’s website it is easy to get information on order and delivery status also for products ordered offline At Berrybenka’s website it is easy to get real-time information on product availability It is easy to search for store locations and opening hours at Berrybenka website Berrybenka sells online the same products as in the physical stores Berrybenka offers the same prices online as in the physical stores Berrybenka offers the same promotions online as in the physical stores
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Construct
Trust
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Measurement Indicators
Adapted Source
Shopping over the online website would be a safe way to shop
[5, 25]
Shopping over the online website would be very risky I would trust online shopping in online website I am prepared to give private information to online companies It is not a problem to pay in advance for purchased products over the internet I follow the brand in all of its activities
Offline Loyalty
I will continue visiting Berrybenka physical store more often to me, it is the best company to do business with
[5, 26]
As long as the present service continues, I doubt that I would switch to another company When I need to use similar service that this company offers, it will be my first choice On this website I can use my loyalty card or redeem coupons obtained offline
Online
On this website I can obtain online coupons to be used offline
Loyalty
When I need to make a purchase, this website is my first choice
[5, 25]
I consider myself to be a loyal patron of the online website I would recommend this company’s website to others I am willing to use Berrybenka when online shopping Repurchase
I plan to use Berrybenka when online shopping
Intention
It is likely that I will repurchase from this Internet store in the near future
[10, 27]
3.4. Sampling This study is measured based on the customer experience during purchasing activity through e-commerce [28]. On the population of this study, where the number and character of the respondent are unknown clearly and this study using non-probability sampling which is purposive sampling technique [29]. The purposive sampling technique is a type of non-probability sampling technique where the researcher chooses the criteria of respondent based on their own criteria that appropriate with the research objective To fully investigate the possible factors affecting online to offline strategy in Berrybenka, a questionnaire form consists of respondent profile and 25 measurement items was created using online survey through Google Form. The survey was delivered mainly through social media by sharing the questionnaire link. The targeted respondents were people who have bought in Berrybenka. The requirement of the respondent was characterized by gender, age, buying experience, and the average transaction in Berrybenka. The period of collecting filled questionnaires took about 2 weeks. From 2153 contacts delivered the questionnaire, 347 provide the response (16, 12% response rate). The amount of valid questionnaire to use for the investigation is 311 and none of the questionnaire is incomplete. 3.5. Data analysis method Factor analysis is used to determine the validity of the construct. Several evaluations are conducted in the measurement, such as KMO, Bartlett test, anti-image, principle component analysis, communalities, total variance explained and rotated component matrix. The validity test is measured to evaluate whether the questionnaire result is representative the sample or not [30]. The data result provided has to be qualified in several criteria of the measurement. In order for the data to be considered valid, The KMO of the data has to above 0.5, the Bartlett has to under 0.5, the anti-image has to be above 0.5, the principle component analysis has to be above 0.5, the communalities has to be above 0.5, the total of variance explained has to be above 60%, and lastly, the rotated component matrix used for the amount of respondents above 200 is 0.4. After the test conduction, it was found that multichannel integration; trust, offline loyalty, online loyalty, and repurchase intention are indeed valid for the measurement. All
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of the valid items should be tested for their reliability afterwards. The qualification for reliability test is the Cronbach’s Alpha has to be greater than 0.6 as stated from [31]. 4. Result and discussion
Fig, 1. Research Framework (with p-value for Hypothesis testing).
SPSS AMOS 23rd version is used for data analysis. In order to test hypotheses about relationship between variables SEM is used (see Fig. 1). First of all, the drawing has to be made and then begin with the analysis. We used Model fit as a comparison between the requirement of data, and the result of the analysis. Based on the result, the model fit the data. The Goodness-of-Fit is measured to determine whether a variable can be accepted or rejected. The seven indicators of Model Fit were used in this study. The cut of value is the criteria of each category. CMIN/DF as a result of value is 3,543, which it was a good fit because it is less than 5. GFI should be more than 0,5 and less than 0,9 but it showed 0,916 so it is poor fit. AGFI had more than 0,5 and less than 0,10 in the results 0,861 so it is marginal fit. Then, IFI criteria, TLI criteria, CFI criteria should be more than 0,9 and the results shows that IFI value showed 0.949, TLI had 0.927 and CFI was 0.948 so it was a good fit. Last, RMSEA’s criteria should be 0.05 ≤ and ≤ 0.08, and the result shown is 0,091 which mean it doesn’t suit with the criteria. All of the model-fit tells exceeded the common acceptance levels proposed by recent research, showing that the structural model displayed a good model fit. Based on our research, we found these data result, our KMO data is 0.900, it is above 0.5 that become a threshold for construct validity. The bartlett data is 0.000, it below 0.5, the communalities should be more than 0,4, and it is more than 0,5 with range 0.734 to 0.917, our total variance explained data is 81.892%, it is above 60%. For rotated component matrix, it should be used 0,4 if more than 200 respondents and some of the component has been eliminated because it didn’t match with the criteria and it show a discriminant issue which causes the removal of some variables, they are Multichannel Integration (1, 2, 3, 4, 5), Trust (3), Offline Loyalty (3), Online Loyalty (3,4,5) and none for Repurchase Intention’s statements. The first variable is multichannel integration with value range from 0.804 to 0.845, then there is trust with value range 0.682 to 0.773, offline loyalty with value range 0.611 to 0.809 and online loyalty with value range 0.864 to 0.890, the last variable is repurchase intention with value range 0.471 to 0.815. The Cronbach’s should be more than 0.6 to ensure the construct reliability. The Cronbach’s alpha data of multichannel integration is 0.877, the Cronbach’s alpha data of trust is 0.812, the Cronbach’s alpha data of offline loyalty is 0.848, the Cronbach’s alpha data of online loyalty is 0.872, and the Cronbach’s alpha data of repurchase intention is 0.927. Table 2. Regression Result. Hypothesis
Estimate
S.E.
C.R.
p-value
Multichannel Integration Offline Loyalty
-1.146
0.347
-3.303
0.000
Multichannel Integration Online Loyalty
-0.830
0.219
-3.790
0.000
Trust Offline Loyalty
2.992
0.587
5.098
0.000
Trust Online Loyalty
2.199
0.354
6.371
0.000
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Hypothesis
Estimate
S.E.
C.R.
p-value
Offline Loyalty Repurchase Intention
1.166
0.126
9.264
0.000
Online Loyalty Repurchase Intention
-0.175
0.80
-2.188
0.029
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According to the appendix Table 2, the P-value is should be less than 0,05 and then the null hypotheses will be accepted, if it’s have more than 0,05 then it could be rejected. The estimates value tells the relations between variables, and the estimates value for Multichannel Integration to Offline Loyalty is -1.146 which means when the value of Multichannel Integration is 1 then the value for Offline Loyalty will be increased 1.146. For the Critical Ration, it is the measurement of parameter estimate divided by an estimate of its standard error. It showed that the strongest correlation of C.R is Trust towards Online Loyalty. It shows how strong the connection between variables, while the sign demonstrates the correlation direction whether it is positive or negative correlation. The higher value of C.R the stronger the correlation will be. The strongest relationship has showed in the Estimate value, when the Estimate value below (d) 0.5, it can assume as weak relationship. As can be seen on the appendix, there is one model that has higher relationships, which is Trust towards Offline Loyalty (0,621). After analyse the data, the result showed that 6 hypotheses are supported as describe above (p<0.05), there are multichannel integration influences offline loyalty, multichannel integration influences online loyalty, trust influences offline loyalty, trust influences online loyalty, offline loyalty influences repurchase intention lastly online loyalty influences repurchase intention [24]. Conducted a research and indicates it result that loyalty towards the offline channel of the multichannel retailer is positively related to loyalty towards its online channel. Another result also found that multichannel integration is positively related to customer loyalty towards the offline and online channel of the retailer. 5. Conclusion and recommendations The results of this research which concern about the influence of multichannel integration, trust and customer loyalty towards repurchase intention an empirical study in Berrybenka shows that; 1) multichannel integration has a significant influence to offline loyalty; 2) multichannel integration has a significant influence to online loyalty; 3) trust has a significant influence to online loyalty; 4) trust has s significant influence to offline loyalty; 5) offline loyalty has a significant influence to repurchase intention; 6) online loyalty has a significant influence to repurchase intention. Nowadays, the competition between fashion retail industries is quite tight. For company, they should do an improvement for their performance. Especially on their online loyalty, because in this research shown that most of customer who already purchased in Berrybenka was preferring to buy in offline. Berrybenka was based online-fashion company, so if their online loyalty didn’t have a significant impact to their customer, how can they compete with the other competitor? Therefore, they can develop another strategy to achieve more market share. Limitations of this research are as follows: The sample of this research only limit to Berrybenka’s online and offline customer who already purchased in Berrybenka and the factors only limits to several factors in O2O strategy. Based on these findings that explore the consequences of multichannel integration, further research may explore the antecedents of multichannel integration that still has limited discussion in information system literature [6, 4, 24]. References [1] [2] [3] [4] [5]
Fajriah, L. R. (2017) A Survey on Online Shopping Rituals during Ramadhan. Available from: https://autotekno.sindonews.com/read/1217460/133/survei-belanja-online-jadi-pilihan-utama-masyarakat-selama-ramadhan-1499010507. [Accessed 2nd July]. Septian, D. (2016) E-commerce in Indonesia Reached 26.2 Million. Available from: http://bisnis.liputan6.com/read/2602680/bps-jumlah-ecommerce-di-indonesia-capai-262-juta. [Accessed 15th September]. Berrybenka. (n. d). (2018) Available from: http://www.berrybenka.com. [Accessed 1st September]. Frasquet, Marta, and Maria-José Miquel. (2017) “Do Channel Integration Efforts Pay-Off in terms of Online and Offline Customer Loyalty?” International Journal of Retail & Distribution Management 45 (7/8): 859-873. Frasquet, Marta, Alejandro Molla Descals, and Maria Eugenia Ruiz-Molina. (2017) “Understanding Loyalty in Multichannel Retailing: The Role of Brand Trsut and Brand Attachment.” International Journal of Retail & Distribution Management 45 (6): 608-625.
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[6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31]
Intan Dewi Savila et al. / Procedia Computer Science 161 (2019) 859–866 Author name / Procedia Computer Science 00 (2019) 000–000
Chatterjee, Patrali. (2012) “Multiple-Channel and Cross-Channel Shopping Behavior: Role of Consumer Shopping Orientations.” Journal of Service Research 28 (1): 9-24. Linton, I. (n.d.). “the Definition of Multi-Channel Retailing.” Available from: http://smallbusiness.chron.com/definition-multichannelretailing-20263.html. Crowell, William. (2012) “Trust, The E-Commerce Difference.” Credit Card Management, 14 (5): 80. Handika. (2015) Trust on Online Transactions. Available from: https://www.kompasiana.com/handika_058/kepercayaan-jual-belionline_5619e93f359373a2048b4574. [Accessed 11st October]. Reast, Jon D. (2012) “Brand Trust and Brand Extension Acceptance the Relationship.” Journal of Product & Brand Management, 14 (1): 4-13. RewardPort. (n.d). “Difference Between Online and Offline Loyalty Programs.” Available from: https://www.rewardport.in/blog/2017/09/01/difference-between-online-and-offline-loyalty-programs/. Farag, S., T. Schwanena, M. Dijsta, and J. Faberb. (2007) “Shopping Online and/Or In-Store? A Structural Equation Model of the Relationships Between E-Shopping And In-Store Shopping.” Transportations Research Part A: Policy and Practice 41(2): 125-141. Hellier, K. Phillip, M. Gus Geursen, A. Rodney Carr, and A. John Rickard. (2003) "Customer Repurchase Intention: A General Structural Equation Model." European Journal of Marketing 37 (11/12): 1762-1800. Bayrakter, Erkan, Ekrem Tatoglu, Ali Turkyilmaz, Dursun Delen, and Selim Zaim. (2012) “Measuring he Efficiency of Customer Satisfaction and Loyalty for Mobile Phone Brands with DEA.” Expert Systems with Applications 39: 99-106. Zeithaml, A. Valarie, L. Leonard Berry, and Ananthanarayanan Parasuraman. (2013) “The Behavioral Consequences of Service Quality.” Jorunal of Marketing 60: 31-46. White, Christopher, and Yi-Ting Yu. (2014) “Satisfaction Emotions and Consumer Behavioral Intentions.” Journal of Services Marketing 19 (6): 411-420. Lee, Wan-I., Shan-Yin Cheng, and Yu-Ta Shih. (2017) “Effects Among Product Attributes, Involvement, Word-Of-Mouth, and Purchase Intention In Online Shopping.” Asia Pasific Management Review 22 (4): 223-229. Meskaran, Fatemeh, Zuraini Ismail, and Bharani Shanmugam. (2013) “Online Purchase Intention: Effect of Trust and Security Perception.” Journal Consumer Behavior 7 (6): 307-315. Sarwar, Muhammad Zaman, Kashif Shafique Abbasi, and Saleem Pervaiz. (2012) “The Effect of Customer Trust on Customer Loyalty and Customer Retention: A Moderating Role of Cause Related Marketing.” Global Journal of Management and Business Research, 12 (6): 2736. Haslinda, H., R. A. Teo Poh Kiong, and Ainuddin. (2014) “Effects of Perceived Value and Trust on Customer Loyalty towards Foreign Banks in Sabah, Malaysia.” Global Journal of Emerging Trends in e-Business 1 (2): 137-153. Shahrokh, Zohreh Dehdashti, Nushin Oveisi, and Seyyed Mahdi Timasi. (2013) “The Effects of Customer Loyalty on Repurchase Intention in B2C E-commerce- A Customer Loyalty Perspective.” Journal Basic and Applied Scientific Research 3 (6): 636-644. Storania. (2014) The Concept of Multichannel Marketing. Available from: https://www.storania.com/pengertian-multichannel-marketing/. [Accessed 27th October]. Rowley, Jenny. (2014) “Designing and Using Research Questionnaires.” Management Research Review 37 (3): 308-330. Frasquet, Marta, and Maria-José Miquel. (2017) “Do Channel Integration Efforts Pay-Off in Terms of Online and Offline Customer Loyalty?” International Journal of Retail & Distribution Management, 45 (7/8): 859-873. Ribbink, Dina., C.R. Allard, Van Riel, Veronica Liljander, and Sandra Streukens. (2004) “Comfort Your Online Customer: Quality, Trust And Loyalty on The Internet.” Managing Service Quality: An International Journal, 14 (6): 446-456. Tsiotsou, and H. Rodoula. (2016) “The Social Aspects of Consumption as Predictors of Consumer Loyalty: Online VS Offline Services.” Journal of Service Management 27 (2): 91-116. Mao, Zhenxing, and Jiaying Lyu. (2017) “Why travelers use Airbnb again? An integrative approach to understanding travelers’ repurchase intention.” International Journal of Contemporary Hospitality Management, 29(9): 2464-2482. Etikan, Ilker, Sulaiman Abubakar Musa, and Rukayya Sunusi Alkassim. (2016) “Comparison Convenience Sampling and Purposive Sampling.” American Journal of Theoritical and Applied Statistics 5 (1): 1-4. Acharya, S. Anita, Anupam Prakash, Pikee Saxena, and Aruna Nigam. (2013) “Sampling: Why and How Of It?” Indian Journal of Medical Specialities 4 (2): 3-7. Bai, Yuling, Cong Li, and Jishun Niu. (2016) “Study on Customer-Perceived Value of Online Clothing Brands.” American Journal of Industrial and Business Management 6 (8): 914-921. Al-Jabaly, M. Sameer, and S. Aymen Khraim. (2014) “The Effect of Perceived Value and Customer Satisfaction on Perceived Price Fairness of Airline Travelers in Jordan.” Universal Journal of Management 2 (5): 186-196.