International Journal of Entrepreneurship (Print ISSN: 1099-9264; Online ISSN: 1939-4675)

Short commentary: 2021 Vol: 25 Issue: 3

Electronic Customer Satisfaction Using Electronic Personalization And Social Media Marketing Model

Al-Hashem Adel Odeh, Al-Balqa Applied University

Abu Orabi Tareq, Al- Balqa Applied University

Citation Information: Odeh AHA, Tareq AO. (2021). Electronic customer satisfaction using electronic personalization and social media marketing model. International Journal of Entrepreneurship, 25(3), 1-11.

Abstract

Electronic personalization and social media considered innovative models used in the marketing strategy to achieve customer satisfaction by adopting modern tools that offer products and services based on personal preferences. The study employed a quantitative approach using a questionnaire distributed to a convenience sample consisted of (622) customers in the five-star hotels in Jordan. (573) questionnaires were valid and tested using AMOS.4. The study results indicated that social media marketing has a significantly positive impact on customer satisfaction and electronic personalization and there is a significantly positive impact of social media marketing on customer satisfaction through the intermediate role of electronic personalization. Accordingly, the management of five stars' hotels should be Adopt electronic marketing tools and models to enhance the level of customer satisfaction in the global competition.

Keywords

Social Media Marketing, Electronic Personalization, Electronic Customer Satisfaction.

Introduction

Social media marketing usage has several advantages for marketers and customers that allow marketers and customers to interact with each other virtually anywhere and anytime (Almasri, 2014). Social media marketing depends on digital technology and tools to exchange data, information and knowledge among the participants in the Social media environment, as the marketing processes through social media became popular and the business value it confirmed based on this study finding and the prior studies. Social media has introduced new business models supported with customer relationship tools, communication tools and advertising tools, because of companies have to replace its traditional marketing campaigns with new ones and exploiting the advantages which provided by social media networks to gain innovative product and service marketing based on customer preferences. Therefore, e- personalization emerged as a new business model relying on digital and advanced technology enables business firms to deliver their products and services more closely to customers’ specifications, websites including social ones use personalization technologies to handle information load in order to provide one- to- one marketing which leads to electronic customer loyalty (Liang et al., 2006). This study tries to bridge Social media marketing usage gap between modern and developed countries especially in Jordan, several companies in Jordan still doesn’t use social media and rely on traditional means in product and service marketing. Thus, the study aims to determine the level of usage of social media marketing, electronic personalization and electronic loyalty. Besides, to explore the relationship between studies constructs and the effect of social media marketing on electronic personalization and electronic loyalty from the customer perspective comparing with prior studies which studied the current constructs of this study separately. The main contribution of the study was an empirical investigation in developing countries especially in Jordan to examine the relationship among the combination of new terms includes digital technology (social media), business model (e-personalization) and e- customer satisfaction which considered the main marketing aim of any business firm.

Background and Hypotheses Bulding

Social Media Marketing

Nowadays, the rapid advancement in technology around word, social networking sites have to provide companies to use direct marketing and reach a wide range of customers to gain better market share (Almasri, 2016). Social media marketing described as a connection between customer and brands through providing new ways for social interaction (Chi, 2011). Social media considered as an innovative tool for online direct marketing without intermediation using digital technology to achieve marketing objectives and bring value to maintain a close relationship with customer. Social media marketing refers to using social media tools an example Facebook, Twitter, to reach customers in an innovative manner for promoting company activities and its brand (Margarita Išoraite, 2016). Multiple channels have been used within social media sites for creating new methods in which business firms and customers can share information and knowledge, features of brands, new products, and services which increase brand awareness. When marketer offer new brand or products, they have to take into account both a traditional and nontraditional means to make sure researching a large number of audience or target market (Cheong and Morrison, 2008), the involvement of business firms in digital media required low investments in comparison with traditional ones, in addition, most programs of social media marketing provide unique content that encourages and attracts the attention of the viewer to exchange and share it (Weinberg and Pehlivan, 2011; AL-Hashem, 2020). Online firms used several communication channels or methods to interact with customers and establishing strong expectations and directly offers their products, different tools and methods used in online direct marketing such as online advertising, banner ads, social media marketing , affiliate marketing (Kaur, 2017). Finally, social Media considered as tools of direct sales, communication, acquisition, and customer retention though which offer for customers possibility to personalize product and services they need (Constantinides, 2014 ) Table 1.Therefore the first and second hypotheses were:

Table 1 Definitions and Description
Variable Definitions and Description Author
Social Media Marketing  Connecting consumer and brand by social interaction using personal channels. (Chi ,2011)
  a set of applications built on Web 2.0 technology enable to sharing and creating the content hat generated by the user. (Kaplan & Haenlein 2010)
  Refers to the brand awareness or supporting website traffic using social media. (Kaur.,2017)

H1: Social media marketing effects on E- personalization.

H2: Social media marketing effects on E- customer satisfaction.

E-Personalization

Personalization practice of products, services and content considered as an innovative business model relying on emerging and advanced information technologies in the rapid today’s business environment (Adomavicius and Tuzhilin, 2005). Electronic business websites retain consumers by offering personalized items according to consumer preferences and track the user behavior and what is clicking on to offer items with similar characteristics (Kobsa, 2007; Krishnaraju et al., 2013). Advanced information technology enables business to tailor their offerings more closely to consumer's tastes and needs to improve the best practices to gain a sustainable competitive advantage of the information technology investments and enhancing the effectiveness of service provided, which leads to customer loyalty (Osterwalder and Pigneur, 2013). Therefore, there are several technologies such as collaborative filtering which meet user' specifications and purchasing behavior online. To create a tight relationship with the customers, the personalization service provider has three mechanisms to provide personalized product and services to create a personalized recommendation, personalized interface and appearing socialness that supports empathic responses to customer (Wang, Y., Kobsa. A, 2007) Web personalization enable business firms to create their offerings to the individual consumer using collected data for designing a website content according to customer preferences in order to create personalized website content to generate value that differentiates itself from other electronic business and electronic service websites (Amit and Zott, 2012). According to the previous studies stated that about 0.80 of users surfing the internet interested in electronic service personalization (Kobsa, 2007) and o.56 online consumers were more likely prefer to purchase on a website that delivers individualized features comparing with other sites, therefore, many websites are competing on unique service features rather than personalization to fit promotion and digital advertising project with customer' preferences (Freedman 2007; Ting-Peng Liang. 2009). Based on previous studies, Kim and Mauborgne, (2005) mentioned the company' website has the ability to create personalized items and differentiates itself from others continuously achieving customer satisfaction which leads to electronic loyalty. Websites use personalization technologies to handle information load to offer one-to-one marketing to gain usable site and better customer satisfaction (Liang et al 2006) Table 2. Thus, the second hypothesis was:

Table 2 Definitions and Description
Variable Definitions and Description Author
E- personalization The process of interactions between the website and a consumer to build a tight relationship which influences on online consumer behavior. (Gore et al. 2006; Li, Yu-Wen. 2009)
  Online tailoring and the presentation of information, product, and service to an individual customer' Preferences. (Linden et al., 2003)
  The process of creating products, services and web content according to the customer specifications. (E. Turban et al., 2011)

H3: E- personalization effects on E-customer satisfaction.

H4: E- personalization mediates the relationship between social media marketing and E-customer satisfaction.

E- Customer Satisfaction

The most successful e- businesses are seeking to deliver high customer satisfaction through retention of their customers and to be more customers oriented. Customer satisfaction is about the experience of customer and how are fulfilled by the service provider, it's related with an individual pursuit or goal that can be achieved from the product/service consumption (Oliver, 2014). Electronic service quality has a significant role in electronic customer satisfaction through reliability, Fulfillment, Graphic style, Privacy/security, Information availability and content and Ease of use (Hsu, 2008), E-customer satisfaction is the level of the customer's feelings compared with the expectation of the product and service provided, if the customer is satisfied, they will return again and become active reference source using multiple communication channels (Buyung Romadhoni, 2015; AL-Hashem,2020). Social media marketing channels are very important tools which support communication, connection and exchange to make a close relationship with a customer to increase customer retention, and satisfaction, the ultimate goal to create a sustainable superior customer value which reflects customer satisfaction (AL-Hashem, 2020; Anjum et al, 2012). Davis et al (1989) mentioned that the level of satisfaction will be increased by providing personalized products and services that are useful and user-friendly Table 3.

Table 3 Definitions and Description
Variable Definitions and Description Author
E- Customer Satisfaction Defined as customer response between initial expectations and actual performance after use. (Tjiptono et al., 2005)
  The customer’s felt level as a result compared with a product’s perceived performance in relation to the expectation of the customer. (Kotler ,2000)
  Electronic satisfaction considered as behavioral of customer perspectives. (Cenfetelli  et al., 2005)

Conceptual Model

The conceptual model describes the relationship between study constructs which include (Social media marketing, E-personalization, E- customer Satisfaction). Social media marketing items developed based on the previous studies by Evans, D, & McKee, J. (2010) and Vipin K. Nadda et al. (2015). E-personalization items adapted by Gaitonde (2008). The items of E- customer Satisfaction adapted by Ong Soo Ting et al., (2016); Siti Hasnah Hassan et al. (2017) Figure 1.

Figure 1 Research Model Methodology

Methodology

The study population comprised of the five stars' hotels customers in Amman city in Jordan, this study used a convenience sampling technique for data collection using a questionnaire as the instrument to gather data for this purpose. A five Likert scale was utilized from (1) strongly disagree to (5) strongly agree. Total of (622) questionnaires were distributed to respondents. Total of (573) questionnaires were returned and valid for analysis (Hair et al., 2010). The proposed model examined using AMOS.4 in two stages: the first one, the measurement model to examine the measurement adequacy of the constructs. The second one, the path model to test the significant relationship among study constructs and fitness of a hypothesized model was examined by other fitness indices include a standardized residual, goodness-of-fit index, modification index, a ratio of chi-square to degrees of freedom, adjusted GFI, comparative fit index, root mean square error of approximation and root mean square residual.

Data Analysis

Descriptive Analysis and Correlation Matrix

As shown in Table 4 descriptive statistics were used for examining the study constructs level in the five stars' hotels in Amman city in Jordan, the highest mean was (3.836) related to e- customer satisfaction(E-CS) followed by social media marketing(SMM) with a value of (3.711) and the lowest level was (3.632) related to e-personalization(E-P). Thus, the level of study constructs (SMM) and (E-CS) was high, contrast (E-P) was medium level. In addition, Bivariate correlation values indicated that social media marketing positively co-related to e-personalization and e-customer's satisfaction.

Table 4 Mean, Standard Deviation and Correlation Matrix.
Constructs Mean S D 1 2 3
Social Media Marketing(SMM) 3.711 1.134 -    
E-Personalization
(E-P)
3.032 0.622 0.296
***
-  
E-Customer Satisfaction (E-CS) 3.836   0.913 0.413
**
0.319
**
-
   **Significant at the 0.05 level, ***Significant at the 0.001 level.

Measurement Model

Structured equation modeling to examine hypothesized model. Therefore, structured equation modeling examines the model consistency, causal relationship among constructs, and instrument items (Jöreskog & Sörbom, 1996). To assess the internal consistency of the proposed model constructs, Cronbach’s Alpha considered a widely used metric for reliability. Table (5) shows high internal consistency for every construct. TO test the measurement model for convergent validity, the values of Average Variance Extracted (AVE) and Composite Reliability (CR) should be higher than 0.5 and 0.7, respectively. For testing discriminant validity of study constructs, the values of Average Shared Variance (ASV) and values of Maximum Shared Variance (MSV) should be less than Average Variance Extracted (AVE) (Hair et al., 2010). Thus, Table 5 shows that all study constructs exhibit a discriminant and convergent validity.

Table 5 Internal Consistency and Validity
  CR AVE MSV ASV Cronbach’s Alpha.
Social Media Marketing 0.912 0.711 0.368 0.177 0.89
E-Personalization 0.833 0.645 0.464 0.214 0.84
E-Customer Satisfaction 0.858 0.598 0.491 0.433 0.88
(CR): Composite Reliability, Average Variance Extracted (AVE), Maximum Shared
Variance (MSV), Average Shared Variance(ASV), Cronbach’s Alpha.

Model Fit Indices

To examine the model fitness table (6) shows that the Comparative Fit Index (CFI) and Goodness of Fit (GFI) have values greater than 0.90 which means a good fit (Hoyle, 1995). The average standardized residual per degree of freedom (RMSEA) value was less than 0.10. Therefore, the results presented have a good level of the model fitness. Confirmatory factor analysis was used as standardized factor loading of all items was greater than 0.40. The items loading from (0.591) with t-value (6.112) to (0.764), t-value (14.923) as shown in table (7).

Table 6 Model Fitness Summary
FIT INDEX MODEL RECOMMENDATION
CFI 0.946717 > 0.90
RMSEA 0.07 < 0.10
GFI 0.93 > 0.90
AGFI 0.82 > 0.80
(TLI) 0.971046 > 0.95
Normed Chi-square 2.65 < 3
Table 7 Factor Loadings and (T-VALUE)
Variable Item No Scale Items Factor loadings (t-value) Author
Social Media Marketing 1 Social media help business firms to spread their goods based on customers' needs. 0.751 (15.722) (Evans & McKee, 2010). (Nadda et al., 2015)
2 Social media helps business firms to collect and share information about customers. 0.691 (14.213)
3 Social media helps businesses to maximize customer attraction and retention. 0.712 (14.993)
4 Social media enable the company to interact with persons who have the same interest. 0.737 (14.587)
5 Social media support company sense and respond to customers messages. 0.652 (9.223)
6 Social media provides hotel the opportunity to reach new customers. 0.619 (6.983)
7 The social media helps businesses to promote themselves. 0.713 (13.705)
8 Social media provides one- to- one marketing with the target audience. 0.615 (9.813)
9 social media creates the opportunity to promote the service. 0.711 (14.513)
10 Social media marketing eliminates the intermediation process. 0.688 (13.991)
E-Personalization 11 The company website offers products according to my preferences. 0.652 (13.560) (Gaitonde, 2008)
12 The company website provides a unique service according to my preferences. 0.6218 (12.681)
13 The company website tailors the web content according to my preferences. 0.694 (14.323)
14 The company website uses my personal information for purpose one- to- one marketing. 0.591 (6.112)
15 The company website automatically personalize e- catalog based on my preferences. 0.634 (7.652)
E-Customer Satisfaction 16 I am happy with the electronic services introduced by the hotel website. 0.764 (14.923) (Ong Soo Ting et al., 2016), (Siti Hasnah Hassan et al., 2017)
17 I am happy with the company product offered on the website. 0.731 (14.966)
18 I am happy with the website's services. 0.625 (6.398)
19 I have enjoyed from online purchasing by the company website. 0.683 (12.101)
20 I feel happy after visiting the company website. 0.611 (6.154)

Hypotheses Testing of Direct and Indirect Effect

As predicted in hypotheses building, table (8) shows that social media marketing has a significantly positive impact on e- personalization (β =0. 0.511, ρ <0.001) and e- customer satisfaction (β =0.241, ρ <0.05) which confirms that there is a significantly positive impact of social media marketing as an independent construct on the dependent constructs include e- personalization and e- customer satisfaction. Hence, H1and H2 was confirmed. Similarly, there is a significantly positive impact of e- personalization on e-customer satisfaction (β =0.320, ρ <0.001). Thus, H3 was confirmed.in addition, the result of an indirect impact of social media marketing on e-customer satisfaction through e-personalization was significant (β =.04.0, ρ <0.05). Therefore, H4was confirmed.

Table 8 Hypothesis Testing
Structural Path Direct Effect t – Value Indirect Effect Total Effect Result
SMM→E-P 0.511*** 10.32 - 0.511*** Confirmed
SMM→ E-CS 0.241** 2.38 0.168 **0.409 Confirmed
E-P→E-CS 0.320*** 3.41 - 0.320*** Confirmed
**Significant: p< .05, ***Significant: p< .001.

Results Discussion and Conclusion

Exploring new business models including social media marketing and its impacts on electronic personalization and electronic customer satisfaction is critical for literature and empirical research in order to provide the best practices in digital marketing and enhance electronic customer satisfaction. This study investigated social media marketing usage in the marketing processes in five stars' hotels in Amman city in Jordan and its role in achieving one- to- one marketing (e- personalization) which leads to customer satisfaction for an individual company website. As predicted in the first and the second hypothesis, the study results confirmed the assumptions that social media marketing has a positively direct impact on e-personalization and electronic customer satisfaction in researched hotels. These findings in line with prior studies which showed the importance of social media as innovative tools used to personalize the products and services and customer retention which lead to electronic customer satisfaction (Constantinides, 2014; Margarit, 2016).Regarding the result of a third hypothesis, e-personalization has a positively direct impact on electronic customer satisfaction which means this finding supports the study assumption and the importance of one- to -one marketing (e-personalization) on electronic satisfaction. This result supported prior studies that addressed the importance of advanced information technologies to tailor their offerings more closely to the consumer’s tastes and needs which results in e-customer satisfaction and e-loyalty characteristics (Kobsa, 2007; Krishnaraju et al., 2013). In addition, Kim and Mauborgne ( 2005) and Liang et al., (2006) stated that companies websites use electronic personalization technology to create personalized items to gain better e- customer satisfaction. The first three hypotheses predicted the direct positive impact between the proposed models constructs, this study moves further with the indirect impact between study constructs, the fourth hypothesis result supports the mediation impact of e- personalization between social media marketing and electronic customer satisfaction. the research findings suggest to companies’ managers should be exploit information technology revolution and social media revolution in particular to gain a better market share through e-personalization and-customer satisfaction. Thus, to increase electronic customer satisfaction level in the researched hotels, managers should be used several models such as e-personalization services, at the same time, enhancing the level of e- personalization to retain the customer.

Limitations and Further Research

The research paper sample was the five stars' hotels customers in Amman city in Jordan who use social media, hence, maybe not generalized to other sectors or in other countries. This study depends on a quantitative approach due to the complexity of study population and the qualitative approach is very hard and needs a long time. The participants’ responses toward social media usage reflects multi-cultural citizens around the word due to the nature of the study sample in five stars' hotels. In addition, privacy issues related to customers and the right to be alone hinder the qualitative approach. Accordingly, the findings of the study set a foundation to encourage researchers to investigate other business models such as customization in order to understand the relationship between all possible variables. Also, in the future works deploying machine learning tolls to predict customers' needs.

References

  1. Adomavicius, G, and Tuzhilin, A. (2005). "Personalization Technologies: A Process-Oriented Perspective," Communications of the ACM, (48:10), pp 83-90. Kobsa A. (2007).  Privacy-enhanced personalization. Communication of the ACM 30:8, pp 24-33.
  2. AL-Hashem Adel Odeh (2020). Mediation Impact of Marketing Intelligence in the Relationship between Technology Based Knowledge sharing and product Innovation. Tem Journal - Technology, Education, Management. Vol.9. No.2.Pp 688-693.
  3. AL-Hashem Adel Odeh. (2020). IT-Based Knowledge Management Processes-Services Innovation and E- Loyalty. Journal of Theoretical and Applied Information Technology. Vol.98, No.10. Pp1765- 1776.
  4. Almasri, A.K.M. (2016). Proposed M-Learning Model Based on Two Models (Technology Acceptance Model and DeLone and McLean IS success Model). International Journal of Computer Applications, 142(4), 5–10. https://doi.org/10.5120/ijca2016909735.
  5. Almasri, A.K.M. (2014). The influence on mobile learning based on technology acceptance model (TAM), mobile readiness (MR) and perceived interaction for higher education students, International Journal of Technical Research and Application, 2(1), 05 11.
  6. ‏Amit, R. and Zott, C. (2012). "Creating Value Through Business Model Innovation", http://marketing.mitsmr.com/PDF/STR0715-Top-10-Strategy.pdf# page=38.
  7. Anjum, A., More, VS, & Ghouri, AM (2012) Social Media Marketing: A Paradigm Shift in Business. International Journal of Economics Business and Management Studies, 1(3), 96-103.
  8. Berlin, Heidelberg. ‏Weinberg, B.D., & Pehlivan, E. (2011). Social spending: Managing the social media mix, Horizons, Volume, May–June 2011, Pages 275-282.
  9. Biz and Bytes, ‏Evans, D., & McKee, J. (2010). Social media marketing. Wiley Publishing. 8, 132-138.
  10. Buyung Romadhoni, Djumilah Hadiwidjojo, Noermijati, Siti Aisjah . (2015). Relationship between-Service Quality, E-Satisfaction, E-Trust E-Commitment In Building Customer E-Loyalty:A Literature Review, International Journal of Business and Management Invention, Volume 4 Issue 2, PP.01-09.
  11. Cenfetelli, R.T., Benbasat, I. and Al-Natour, S. (2005) Information technology mediated customer service: A functional perspective. In Proceedings of the 26th international conference on information systems, Las Vegas, Nevada, United States, 11-14 December 2005, 725–739.
  12. Cheong, H. J., & Morrison, M.A. (2008). Consumers’ reliance on product information and recommendations found in UGC. Journal of Interactive Advertising, 8(2), 38-49. ‏
  13. Chi, Hsu-Hsien. (2011). Study of User Motivation and Social Media Marketing Responses in Taiwan. Journal of Interactive Advertising, 12, 44-6.
  14. Constantinides, E. (2014). Foundations of social media marketing. Procedia-Social and behavioral sciences, 148, 40-57. ‏
  15. Davis, F.D., Bagozzi, P.R., & Warshaw P.R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8). 983-1003.
  16. Freedman, L., & Listen, L.L. (2007). Merchant Views of personalization and lasting customer relationships. Special report, ATO. ‏
  17. Gaitonde, R. (2008). The three degrees of personalization. Online Article.‏.” ((http://www.rahulgationde.org).
  18. Gore, J. S., Cross, S.E., & Morris, M.L. (2006). Let's be friends: Relational self-construal and the development of intimacy. Personal Relationships, 13(1), 83-102. ‏
  19. Hair, J.F., Black, W.C., Babin, B. J., Anderson, R.E., & Tatham, R. (2006). Multivariate data analysis. Upper saddle River. ‏
  20. Hoyle, R.H. (1995). The structural equation modelling approach: Basic concepts and fundamental issues. Thousand Oaks, CA: SAGE Publications.
  21. Hsu, S.H. (2008). Developing an index for online customer satisfaction: Adaptation of American Customer Satisfaction Index. Expert systems with Applications, 34(4), 3033-3042. ‏
  22. Išoraite, M. (2016). Marketing mix theoretical aspects. International Journal of Research-Granthaalayah, 4(6), 25-37. ‏
  23. Jöreskog, K.G., & Sörbom, D. (1996). PRELIS 2 user's reference guide: A program for multivariate data screening and data summarization: A preprocessor for Lisrel. Scientific Software International. ‏
  24. Kaplan, Andreas M. and Michael Haenlein. (2010). “Users of the World, Unite! The Challenges and Opportunities of Social Media.” Business Horizons 53: 59-68.
  25. Kaur, M.T. (2017). Online Marketing Communication. Biz and Bytes, 8, 132-138.‏
  26. Kim, W.C., & Mauborgne, R. (2005). Blue Ocean Strategy. Boston: Harvard Business School Press.
  27. Kobsa A. (2007). “Privacy-enhanced personalization. Communication of the ACM, 30, 8, 24-33.
  28. Kotler, P, (2000), Marketing management: The millenium edition. UpperbSadlle River, N.J.: Prentice-Hall International, Inc.
  29. Krishnaraju, V., Mathew, S.K. and Sugumaran, V. (2013), "Role of Web Personalization in Consumer Acceptance of E-Government Services", in Americas Conference on Information Systems, Chicago, Vol. 19.
  30. Liang, T.P., Lai, H.J., and Ku, Y.C. (2006). “Personalized content recommendation and user satisfaction: theoretical synthesis and empirical findings.  "Journal of Management Information Systems, 23, 3, 45-70.
  31. Linden, G., Smith, B., & York, J. (2003). Amazon.com Recommendations Item-to-Item Collaborative Filtering. IEEE Internet Computing, 7(1), 76–80.
  32. Nadda, V. K., Dadwal, S. S., & Firdous, A. (2015). Social media marketing. In Handbook of Research on Integrating Social Media into Strategic Marketing (pp. 359-379). IGI Global.
  33. Oliver, R.L. (2014), Satisfaction: A Behavioral Perspective on the Consumer. 2nd ed. New York: Routledge.
  34. Ong Soo Ting, Mohd Shoki Md Ariff, Norhayati Zakuan, Zuraidah Sulaiman and Muhamad Zameri Mat Saman.(2016).E-Service Quality, E-Satisfaction and E-Loyalty of Online Shoppers in Business to Consumer Market; Evidence form Malaysia, IOP Conference Series: Materials Science and Engineering, Volume 131.
  35. Osterwalder, A. and Pigneur, Y. (2013), "Business Model Generation: A Handbook for Visionaries Game Changers, and Challengers", Wiley.
  36. ‏Siti Hasnah Hassan, T. Ramayah Thurasa, Wai Yee Loi. (2017). E-lifestyle, Customer Satisfaction and Loyalty among Mobile Subscribers in Thailand. International Review of Management and Marketing, 7(1), 354-362.
  37. Ting-Peng Liang. Hsin-Yi Chen. Efraim Turban. (2009). Effect of personalization on the perceived usefulness of online customer services: A dual-core theory, ICEC ’09, Taipei, Taiwan.
  38. Tjiptono, F., & Chandra, G. (2005). Service, Quality & SatisfactionYogyakarta. Andi Yogyakarta.
  39. Turban, E., Liang, T.P., & Wu, S.P. (2011). A framework for adopting collaboration 2.0 tools for virtual group decision making. Group Decision and Negotiation, 20(2), 137-154. ‏
  40. Wang, Y., & Kobsa, A. (2007, July). Respecting users’ individual privacy constraints in web personalization. In International Conference on User Modeling (pp. 157-166). Springer.
  41. Wen. Li. Yu. (2009). "Personalization as a Strategy to Build Customer Relationship: The Role of Intimacy. "PACIS 2009 Proceedings. 97.
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