=Paper=
{{Paper
|id=Vol-3026/paper22
|storemode=property
|title=What Digital Content Marketing Works for E-Commerce platforms? An Analysis of Customers’ Passive Use in Enhancing Purchase Intention
|pdfUrl=https://ceur-ws.org/Vol-3026/paper22.pdf
|volume=Vol-3026
|authors=Giang My Chu,Nhu Thi Thao Nguyen,Minh Hai Khuong
}}
==What Digital Content Marketing Works for E-Commerce platforms? An Analysis of Customers’ Passive Use in Enhancing Purchase Intention==
https://ceur-ws.org/Vol-3026/paper22.pdf
What digital content marketing works for e-commerce
platforms? An analysis of customers’ passive use in
enhancing purchase intention
Giang My Chu [0000-0001-9509-3518], Nhu Thi Thao Nguyen[0000-0003-2467-3691] and Minh
Hai Khuong[0000-0002-7620-5999]
The University of Danang - University of Economics, Da Nang, Vietnam
giangcm@due.edu.vn,nttnhuz@gmail.com,mikhuongg@gmail.com
Abstract. Drawn on Uses and gratifications theory (UGT), this study analyzes
the influence of customers’ digital content marketing (DCM) motivations on their
purchase intention on e-commerce platform. By using quantitative methods with
a sample size of 414 young customers and Partial Least Squares (PLS-SEM) to
test the conceptual model, the research results indicated that information, enter-
tainment and remuneration have a positive impact on customer passive use,
which can lead to purchase intention. Thus, our study extends the UGT research
by introducing a theoretical framework of DCM and advancing our understand-
ing of how to promote online purchase intention. Our framework may provide
managers insights into their digital marketing strategy and help them make use
of customers’ passive use e-commerce platforms to enhance sales.
Keywords: Digital content marketing (DCM), UGT, Passive use, Purchase in-
tention, E-commerce
1 Introduction
The Internet is developing rapidly that provides brand opportunities to sell products to
customers through e-commerce platforms (Fan et al., 2020). According to a report of
We Are Social (2021), there were 45.6 million Vietnamese people purchasing products
via the Internet in 2020. It is obvious that Vietnamese customers are getting used to
online shopping, especially in Covid-19 pandemic period. Thus, how to attract and keep
customers purchasing with this volume is a main concern for e-commerce managers.
Regarding online shopping, Pavlou (2003) defined purchase intention as customers’
willingness to engage in an online transaction, which can drive in more sales. While
Copyright © by the paper’s authors. Use permitted under Creative Commons License Attribu-
tion 4.0 International (CC BY 4.0). In: N. D. Vo, O.-J. Lee, K.-H. N. Bui, H. G. Lim, H.-J.
Jeon, P.-M. Nguyen, B. Q. Tuyen, J.-T. Kim, J. J. Jung, T. A. Vo (eds.): Proceedings of the
2nd International Conference on Human-centered Artificial Intelligence (Computing4Human
2021), Da Nang, Viet Nam, 28-October-2021, published at http://ceur-ws.org
Corresponding Author.
200 Chu et al.
competition among online vendors becomes stronger, understanding customer behavior
through purchase intention as well as the effective way to communicate with them is
gaining importance. The report of We Are Social (2021) also pointed out that 85.5 per-
cent of Vietnamese Internet users have already searched for a product to buy. However,
few recent studies relating to online purchase intention have investigated how to en-
hance customers’ intention to buy via providing value content. Consequently, the e-
commerce managers still have no instructions to deliver brand-related content to cus-
tomers based on their needs to promote them to buy products. Thus, our study will fill
this gap by exploring the way to develop customers’ purchase intention via their passive
use, considering if there is a difference among males and females. Hence, the purpose
of our study is to (1) enhance understanding of customer motivation relating to brand’s
DCM; (2) develop a conceptualization of DCM in e-commerce; (3) suggest a guidance
regarding efficient DCM strategy for brand managers on e-commerce platforms.
2 Conceptual background
2.1 Digital content marketing
Customers are becoming to doubt advertising and other traditional marketing commu-
nications, thus setting the stage for the growth of DCM (Matteo & Dal Zotto, 2015).
Breidbach et al. (2014) argued that DCM represents content marketing activities per-
formed through digital platforms (e.g., company website, social media) in different for-
mats such as live streaming/video, guides, and others (Taylor, 2012). In e-commerce
platforms (e.g., Shopee), DCM can be displayed on brands' home page or product in-
formation. In addition, Wang et al. (2017) considered DCM can boost customer convert
to brand outcomes such as brand awareness, engagement, brand trust, and sales leads
(Holliman & Rowley, 2014). Thus, DCM is becoming more important; however, there
are few papers investigating its role in e-commerce platforms. To fill this gap, this study
considers DCM as “the creation and dissemination of relevant, valuable brand-related
content to current or prospective customers on digital platforms to develop their favor-
able brand engagement, trust, and relationships” (Hollebeek & Macky, 2019).
2.2 Uses and Gratification theory
Uses and gratifications theory (UGT) has been widely applied to assess people's moti-
vation to use media. In contrast to effects-oriented research traditions that take the view
of the communicator, UGT examines media effects from the perspective of the individ-
ual user (Aitken et al., 2008) by exploring why and how they use a media rather than
how the media influence them (Katz et al., 1974). UGT has been applied in many re-
search areas to explore communication tool usage (Conway & Rubin, 1991; Dimmick
et al., 2000). Similarly, this study considers UGT as the theoretical background to un-
derstand why customer consume DCM. Because brands target to attract customers by
creating and distributing value content, it’s important to explore customers’ motivations
to build suitable marketing strategies. Thus, this study applies the UGT classification
What digital content marketing works for e-commerce platforms? 201
of Piehler et al. (2019) to comprehend the DCM motivations through four factors: in-
formation, entertainment, social interaction, and remuneration, which are the most ap-
plied to understanding the customers' activities relating to brand-related content.
2.3 Passive use
E-commerce platforms have added several functions which allow customers to con-
sume brand-related content from firms or other customers (e.g., Shopee Feed of Sho-
pee). Muntinga et al. (2011) argued that these activities represent the engagement of
passive customers, who consume by reading reviews or following home pages of a
brand. Thus, by consuming brand-related content on e-commerce platforms, customers
can satisfy their needs through the brand's DCM. Gainous et al. (2021) considered pas-
sive use to have the same meaning as “consumption” of social media, which was de-
fined by Muntinga et al. (2011). Thus, this research argues that the literature relating to
content consumption can be used to build the theoretical basis of passive use.
2.4 Purchase intention
Most industries concentrate on enhancing customers’ purchase intention. In the context
of e-commerce, purchase intention is the state when a customer is willing to enter into
an online transaction (Pavlou, 2003). Zhang et al. (2014) argued measuring actual be-
haviors is difficult so purchase intention is a good factor to represent actual behavior.
Moreover, lack of intention to buy on online platforms creates many barriers in the
progress of e-commerce (He et al., 2008). Hence, investigating purchase intention in
the e-commerce context is essential to have a better understanding of the needs and
expectations of customers (Shaari & Arifin, 2010). Therefore, this study considers pur-
chase intention as the process that customers plan to buy a product or service (Huarng
et al., 2010) based on information obtained from the company’s DCM strategies.
3 Hypothesis development
3.1 Digital content marketing and Passive use
Digital content marketing motivations are considered to drive positively customers’
passive use. Foremost, information motivation refers the customers' search for infor-
mation about products, reviews of others or any information relating to their purchase
or use of the brand's products (Muntinga et al., 2011). Thus, lack of information about
a product or brand can lead to customers’ informative needs, which result in content
consuming activities. Next, entertainment motivation involves relaxation, escapism,
amusement and fun (Muntinga et al., 2011; de Vries & Carlson, 2014). Hence, custom-
ers tend to consume brand’s hilarious posts to escape from reality or have fun. Third,
social interaction motivation includes satisfaction in relation to others, such as social
identity (accomplishing a sense of connection, belonging). According to previous stud-
ies (Mael & Ashforth, 1992; Popp & Wilson, 2018), customers can find a belonging
feeling by browsing a brand’s homepage. Thus, social interaction motivation can lead
to content consuming behavior. Finally, remuneration relates to economic rewards such
202 Chu et al.
as gifts, coupons and discounts (Tsai et al., 2017). To receive them, customers have to
consume brand-related content first. Consequently, the more remuneration motivation
customers have, the more content they consume. Hence, the hypotheses are proposed:
H1: (a) Information, (b) Entertainment, (c) Social interaction, and (d) Remuneration
respectively has a positive relationship with Passive use.
3.2 Passive use and Purchase intention
According to Flynn et al. (1996), customers regularly seek guidance and permission
before deciding to buy a product. Thus, they tend to browse an online community or
read reviews from others to obtain necessary information. As such when customers are
gratified by consuming brand-related content, they will have more willingness to buy.
Additionally, Pöyry et al. (2013) point out that when customers repeat browsing brand
home page, customers will visit online communities with stronger needs to purchase in
mind. This study argues that e-commerce users (also online customers) can be enhanced
purchase intention through consuming brand-related content.
Moreover, some consumer behavior analyses show that males and females differ in
information processing (Palmer & Bejou, 1995). While females are more concerned
with the risk of buying online (Garbarino & Strahilevitz, 2004), males tend to be risk-
taking and take benefits from these activities (Fan & Miao, 2012). They are also told
that “females did not trust e-commerce to the same extent as males did” (Cyr & Bo-
nanni, 2005). Furthermore, in the context of the physical environment, females are more
sensitive than males (Chiu et al., 2005). It is obvious that female customers will focus
more on available information on e-commerce to understand a product or brand than
males, thus leading to higher purchase intention. Thus, the hypotheses are developed:
H2: Passive use has a positive impact on purchase intention.
H3: The relationship between passive use and purchase intention is stronger for fe-
males than for males.
4 Research method
This study conducted a survey through questionnaires to collect primary data. The ques-
tionnaire aims to collect customers’ opinion about DCM motivation, their passive use
and purchase intention on Shopee. The questionnaire was built in English, then trans-
lated to Vietnamese. It consists of three parts. Specifically, part 1 provides an introduc-
tion, part 2 is to obtain respondent’s personal information, and part 3 includes all items
to measure six constructs. The scale items were measured by a Likert scale ranging
from 1 to 5 to represent “strongly disagree” to “strongly agree” and were adopted from
previous studies. Particularly, DCM consists of four factors adopted from de Vries et
al. (2017); Passive use’s scales were developed by Schivinski et al. (2016) and purchase
intention were from Schivinski and Dabrowski (2016). Besides, the respondents were
What digital content marketing works for e-commerce platforms? 203
over 18 years old from The University of Danang - University of Economics as well as
followers of Nescafé on Shopee platform. Moreover, to perform and analysis data, this
study used Microsoft Excel, SPSS version 24 and SmartPLS 3.0.
5 Data analysis
5.1 Demographic
In total, most (69.1%) of respondents are from 18 to 20, 52.4% of respondents have
monthly income from 1 million VND to 5 million VND. Due to the aim of examining
the moderator effect of gender on purchase intention, the number of male and female
respondents is nearly equal (203 men and 211 women). Moreover, most (88.9%) of
respondents confirmed to access Shopee regularly every day.
5.2 Testing the measurement model
Construct reliability was measured through testing the extent to which items are free of
random errors and expose persistent results. When Cronbach’s alpha and Composite
reliability (CR) of all constructs are larger than 0.7, the standard of reliability is ac-
cepted (Nunnally, 1978). In this study, Cronbach’s alpha of all constructs was greater
than 0.7 and CRs ranged from 0.879 to 0.922.
Convergent validity was measured by assessing via two criteria: (1) factor loading
of each item exceeded 0.7 (Agarwal & Karahanna, 2000), and (2) averaged variance
extracted (AVE) should be larger than 0.5 (Fornell & Larchker, 1981). The results in-
dicated that two items of Information (IN6, IN7) and two items of Social interaction
(SI3, SI4) were dropped. All factor loadings of remaining items were greater than 0.7
and AVE ranged from 0.622 to 0.782.
Discriminant validity was measured using the square root of the AVE by a structure
from its indicators that must exceed that structure's correlation with other structures
(Fornell & Larcker, 1981). The results showed that the square roots of AVE are greater
than the off-diagonal elements present. In summary, this model meets both standards
of reliability and validity, thus accepting the measurement model.
5.3 Testing the structural model
According to Henseler et al. (2014), SRMR (Standardized Root Mean Square Residual)
is a good indicator to assess the suitability of a model to its sample. This index is ex-
pected to be <0.08 or 0.1 (Hu & Bentler, 1999). The PLS analysis showed that SRMR
(0.061) was less than 0.1. Besides, the results also indicated the coefficient of determi-
nation (R square) equal to 0.375 and larger than 0.2, which is considered as high in
consumer behavior domain (Hair et al., 2011). The hypothetical structural model is ex-
amined based on the significance levels and path coefficient of each factor. This study
204 Chu et al.
applied the path coefficient significance levels of ∗p < 0.05, ∗∗p < 0.01 to test the hy-
potheses.
Fig. 1. Results of analysis of the structural model.
Table 1. Summary of hypothesis testing results.
Hypothesis Original T-statistics P-value Conclude
samples
H1a: Information has a positive rela- 0.285 4.894 0.000 Supported
tionship with passive use
H1b: Entertainment has a positive re- 0.211 2.558 0.011 Supported
lationship with passive use
H1c: Social interaction has a positive 0.056 0.962 0.336 Not supported
relationship with passive use
H1d: Remuneration has a positive re- 0.233 3.609 0.000 Supported
lationship with passive use
H2: Passive use has a positive impact 0.563 4.679 0.000 Supported
on purchase intention.
H3: The relationship between passive 0.032 0.381 0.704 Not supported
use and purchase intentions is
stronger for females than for males.
6 Discussion
The results have confirmed the relationships between customers’ motivation to use
DCM and purchase intention via their passive use in the e-commerce context, consid-
ering the moderating effect of customers’ gender. This study explored that information,
entertainment, and remuneration affect positively on their passive use on e-commerce
platforms, while social interaction has no effect. This relationship is in line with the
What digital content marketing works for e-commerce platforms? 205
study of Muntinga et al. (2011). In terms of information, customers are becoming de-
pending on others’ experiences about a brand/product, which leads to more regularity
in their content consumption. Besides, customers who have higher entertainment moti-
vation tend to consume brand-related content. This can be explained by the strong im-
pact of Covid-19 pandemic, which gives most people fear and facilitates them to pass
time and escape from reality. Moreover, Piehler et al. (2019) stated that remuneration
content requires a more active level of customers’ participation (e.g., commenting,
sharing), thus remuneration has no effect on content consumption. This study argued
that before customers have more active use, they have to consume DCM passively.
Hence, remuneration affects passive use positively.
In contrast, when social interaction is well drawn in previous social media studies
(Piehler et al., 2019), the result showed that this motivation is not relevant to customers’
passive use (p = 0.336). This can be explained by some different features between social
media and e-commerce platforms. While social media has brand community function
to promote customers to consume brand-related content then interact with others, e-
commerce platforms don’t have a place for customers to share content. Thus, customers
tend to choose social media when they want to interact through a brand's DCM.
The result also showed that customers’ passive use influences positively on their
purchase intention in the context of e-commerce, which is consistent with the outcomes
of previous social media studies (Pöyry et al., 2013; Qin, 2020). However, gender has
no moderating effect on the relationship between passive use and purchase intention (p
= 0.704). This implies that both male and female customers have similar content con-
suming behavior and purchase intention on e-commerce platforms.
7 Implication and limitation
Based on the research results, this study provides some practical implications. First,
besides adding three characteristics (e.i. information, entertainment, and remuneration)
into the brand's DCM to facilitate customer passive use, brand managers on e-com-
merce platforms should promote customer feedback about product/brand via remuner-
ation (e.g., gifts, coupons) to enrich the source of reliable information. Second, e-com-
merce developers should aim to create new features such as group/community to im-
prove social interaction among customers, which can lead to their passive use (Piehler
et al., 2019) and purchase intention. Third, managers should pay attention to both male
and female customers, due to their similar passive use on e-commerce platforms.
This study still has some limitations. First, only gender was examined as a moderat-
ing variable between customers’ passive use and purchase intention. Further research
can consider other variables such as ages, incomes, etc. Second, the research was con-
ducted in only Danang, the generalisability of these findings is limited. Thus, there is a
need to examine the research in other cities or nations. Third, this study only accessed
206 Chu et al.
customers’ purchase intention, further research should explore their actual online be-
havior.
References
1. Agarwal, R., Karahanna, E.: Time flies when you're having fun: Cognitive absorption and
beliefs about information technology usage. MIS quarterly, 665-694 (2000).
2. Aitken, R., Gray, B., Lawson, R.: Advertising effectiveness from a consumer perspective.
International Journal of Advertising 27(2), 279-297 (2008).
3. Breidbach, C. F., Brodie, R., Hollebeek, L.: Beyond virtuality: from engagement platforms
to engagement ecosystems. Managing Service Quality (2014).
4. Chiu, Y. B., Lin, C. P., Tang, L. L.: Gender differs: assessing a model of online purchase
intentions in e‐tail service. International journal of service industry management (2005).
5. Conway, J. C., Rubin, A. M.: Psychological predictors of television viewing motivation.
Communication Research 18(4), 443-463 (1991).
6. Cyr, D., Bonanni, C.: Gender and website design in e-business. International Journal of
Electronic Business, 3(6), 565-582 (2005).
7. De Vries, N. J., Carlson, J.: Examining the drivers and brand performance implications of
customer engagement with brands in the social media environment. Journal of Brand Man-
agement 21(6), 495-515 (2014).
8. Dimmick, J., Kline, S., Stafford, L.: The gratification niches of personal e-mail and the
telephone: Competition, displacement, and complementarity. Communication research,
27(2), 227-248 (2000).
9. Fan, X., Yin, Z., Liu, Y.: The value of horizontal cooperation in online retail channels.
Electronic Commerce Research and Applications 39, 100897 (2020).
10. Fan, Y-W., Miao, Y-F.: ‘Effect of electronic word-of-mouth on consumer purchase inten-
tion: the perspective of gender differences’, International Journal of Electronic Business
Management 10(3), 175–181 (2012).
11. Flynn, L. R., Goldsmith, R. E., Eastman, J. K.: Opinion leaders and opinion seekers: Two
new measurement scales. Journal of the academy of marketing science 24(2), 137-147
(1996).
12. Fornell, C., Larcker, D. F.: Structural equation models with unobservable variables and
measurement error: Algebra and statistics. Journal of Marketing Research (18), 382-388
(1981).
13. Gainous, J., Abbott, J. P., Wagner, K. M.: Active vs. passive social media engagement with
critical information: Protest behavior in two Asian countries. The International Journal of
Press/Politics 26(2), 464-483 (2021).
14. Garbarino, E., Strahilevitz, M.: Gender differences in the perceived risk of buying online
and the effects of receiving a site recommendation. Journal of Business Research 57(7),
768-775 (2004).
15. Hair, J. F., Ringle, C. M., Sarstedt, M.: PLS-SEM: Indeed a silver bullet. Journal of Mar-
keting theory and Practice 19(2), 139-152 (2011).
16. He, D., Lu, Y., Zhou, D.: Empirical study of consumers' purchase intentions in C2C elec-
tronic commerce. Tsinghua Science & Technology 13(3), 287-292 (2008).
17. Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D.
W., Ketchen, D. J., Hair, J. F., Hult, G. T. M., Calantone, R. J.: Common beliefs and reality
about PLS: Comments on Rönkkö and Evermann (2013). Organizational research methods
17(2), 182-209 (2014).
What digital content marketing works for e-commerce platforms? 207
18. Hollebeek, L. D., Macky, K.: Digital content marketing's role in fostering consumer en-
gagement, trust, and value: Framework, fundamental propositions, and implications. Jour-
nal of Interactive Marketing 45, 27-41 (2019).
19. Holliman, G., Rowley, J.: Business to business digital content marketing: marketers’ per-
ceptions of best practice. Journal of research in interactive marketing (2014).
20. Hu, L. T., Bentler, P. M.: Cutoff criteria for fit indexes in covariance structure analysis:
Conventional criteria versus new alternatives. Structural equation modeling: a multidisci-
plinary journal 6(1), 1-55 (1999).
21. Huarng, K. H., Yu, T. H. K., Huang, J. J.: The impacts of instructional video advertising on
customer purchasing intentions on the Internet. Service Business 4(1), 27-36 (2010).
22. Katz, E., Blumler, J.G., Gurevitch, M.: Utilization of mass communication by the individ-
ual, in Blumler J.G., Katz, E. (eds) The Uses of Mass Communications: 43 Introducing
COBRAs Current Perspectives on Gratifications Research, Vol. 3. Beverly Hills, CA: Sage
Publications, pp. 19–32 (1974).
23. Mael, F., Ashforth, B. E.: Alumni and their alma mater: A partial test of the reformulated
model of organizational identification. Journal of organizational Behavior 13(2), 103-123
(1992).
24. Matteo, S., Dal Zotto, C.: Native advertising, or how to stretch editorial to sponsored con-
tent within a transmedia branding era. In Handbook of media branding (pp. 169-185).
Springer, Cham (2015).
25. Muntinga, D. G., Moorman, M., Smit, E. G.: Introducing COBRAs: Exploring motivations
for brand-related social media use. International Journal of advertising 30(1), 13-46 (2011).
26. Nunnally, J. C.: Psychometric theory. 2nd edn. McGraw-Hill, New York (1978).
27. Palmer, A., Bejou, D.: The effects of gender on the development of relationships between
clients and financial advisers. International Journal of Bank Marketing (1995).
28. Pavlou, P. A.: Consumer acceptance of electronic commerce: Integrating trust and risk with
the technology acceptance model. International journal of electronic commerce 7(3), 101-
134 (2003).
29. Piehler, R., Schade, M., Kleine-Kalmer, B., Burmann, C.: Consumers’ online brand-related
activities (COBRAs) on SNS brand pages: An investigation of consuming, contributing and
creating behaviours of SNS brand page followers. European Journal of Marketing (2019).
30. Popp, B., Wilson, B.: “Investigating the role of identification for social networking Face-
book brand pages”, Computers in Human Behavior Vol. 84, pp. 141-152 (2018).
31. Pöyry, E., Parvinen, P., Malmivaara, T.: Can we get from liking to buying? Behavioral
differences in hedonic and utilitarian Facebook usage. Electronic Commerce Research and
Applications 12(4), 224-235 (2013).
32. Qin, Y. S.: Fostering brand–consumer interactions in social media: the role of social media
uses and gratifications. Journal of Research in Interactive Marketing, (2020).
33. Schivinski, B., Christodoulides, G., Dabrowski, D: Measuring consumers' engagement with
brand-related social-media content: Development and validation of a scale that identifies
levels of social-media engagement with brands. Journal of advertising research 56(1), 64-
80 (2016).
34. Schivinski, B., Dabrowski, D.: The effect of social media communication on consumer per-
ceptions of brands. Journal of Marketing Communications 22(2), 189-214 (2016).
35. Shaari, J. A. N., Mohd Arifin, N. S.: Dimension of halal purchase intention: A preliminary
study (2009).
36. Taylor, Gabriela, Digital Content Marketing. USA: Create Space Independent Publishing
Platform (2012).
208 Chu et al.
37. Tsai, W. H. S., Men, L. R. Consumer engagement with brands on social network sites: A
cross-cultural comparison of China and the USA. Journal of Marketing Communications
23(1), 2-21 (2017).
38. Wang, W. L., Malthouse, E. C., Calder, B., Uzunoglu, E.: B2B content marketing for pro-
fessional services: In-person versus digital contacts. Industrial marketing management 81,
160-168 (2019).
39. We are social, https://datareportal.com/reports/digital-2021-vi-
etnam?fbclid=IwAR0qlc6YJY1ZEa_lXCnc7G7Ax7I5lLCkLqgsu-
MmQhqAcVtid7TazoMVkEY, last accessed 2021/06/25.
40. Zhang, H., Lu, Y., Gupta, S., Zhao, L.: What motivates customers to participate in social
commerce? The impact of technological environments and virtual customer experiences.
Information & Management 51(8), 1017-1030 (2014).