Access to this full-text is provided by IGI Global Scientific Publishing.
Content available from Journal of Global Information Management
This content is subject to copyright. Terms and conditions apply.
DOI: 10.4018/JGIM.325228
Volume 31 • Issue 2
This article published as an Open Access article distributed under the terms of the Creative Commons Attribution License
(http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and production in any medium,
provided the author of the original work and original publication source are properly credited.
*Corresponding Author
1
Yanshu Ji, Nanjing University, China*
Wenjing Song, Nanjing University, China
Lei Yang, Nanjing University, China
Chunyan Jiang, Nanjing University, China*
More and more brands are utilizing online word-of-mouth recommendations to promote their products
and using product ingredients more widely in marketing communications. However, no research
has explored how recommendation terms (ingredient vs. function) for functional products affects
consumers’ attitudes. Through one pilot study and four experiments, it was found that consumers
hold the lay belief that “ingredient = professional.” For functional products, consumers’ purchase
intentions and product evaluations are higher when ingredient (vs. function) are labelled, with perceived
trustworthiness playing a mediating role. In addition, the domain knowledge of the information
sender moderates the focal effect. Theoretically, this research contributes to the literature on both
lay beliefs and word-of-mouth marketing by proposing a new lay belief, and by exploring the effect
of recommendation terms on consumers’ attitudes. Practically, this research can guide marketers in
the appropriate use of product ingredient for word-of-mouth marketing.
Functional Products, Perceived Trustworthiness, Product Evaluation, Product Ingredient, Purchase Intention,
Word-of-Mouth Recommendations
Marketers are increasingly utilizing product ingredients as a method to promote product functionality,
with the ultimate goal of influencing consumer perceptions and purchase intentions. “Niacinamide,”
for instance, is mentioned to highlight the whitening properties of cosmetics, and “resveratrol”
is mentioned to highlight the anti-aging properties of pharmaceuticals. Research has shown that
functional products in many categories, such as cosmetics, health products, and personal care products,
have begun to emphasize the role of product ingredients (Lellis, 2016; Xu & Wyer, 2010). In addition,
Volume 31 • Issue 2
2
with the popularity of social media, brands promote their products or services through online word-of-
mouth recommendations via users on social media (Ki & Kim, 2019). This type of recommendation
has become an efficient new marketing tool due to its viral speed of information dissemination (Stubb
et al., 2019). However, to the best of our knowledge, no research has been conducted to investigate the
application of product ingredients in the online word-of-mouth recommendation context. With this
background, influencer approaches to content output, such as whether to use functions or ingredients
as recommendation terms for functional products, are supposed to influence consumers’ purchase
intentions and product evaluations, but little research has paid attention to them.
Based on this premise, this research intends to delve deeper into the aforementioned issues.
Research on lay beliefs shows that when two things in life frequently occur at the same time, individuals
will gradually establish a connection between them, leading to the formation of lay beliefs (Zane
et al., 2020), which is a very important consumer cognitive schema. This research proposes that
because ingredients often co-occur with professional-related concepts (e.g., people with professional
knowledge, professional content), consumers will hold a lay belief that “ingredient = professional.”
Consumers will therefore rely on the lay belief of “ingredient = professional” in the context of online
word-of-mouth. In other words, labeling the ingredients of a product will cause consumers to perceive
the content of word-of-mouth information more related to the concept of “professional” than directly
labeling the functions of a product. This will increase the consumers’ perceived trustworthiness of
the information sender and the content of the message, which in turn increases purchase intention
and product evaluation. In addition, when the domain knowledge of the information sender is high,
consumers rely on the trustworthiness of the sender to make their judgments, independent of the
recommended terms. Conversely, when the domain knowledge of the information sender is low,
consumers perceive a mismatch between the low domain knowledge of the sender and the high domain
knowledge required to label ingredients, resulting in lower purchase intentions and product evaluations.
This research first verified the existence of the lay belief that “ingredient = professional” in
consumers’ minds by using two implicit measurement methods in a pilot study. Then, Study 1 tested
the main hypothesis that labeled ingredients trigger higher purchase intention than labeled functions
in word-of-mouth recommendations. Study 2 verified the mediating role of perceived trustworthiness.
Study 3 extended the effect of recommendation terms from purchase intention to product evaluation
and reaffirmed the mediating role of perceived trustworthiness. Study 4 demonstrated the moderating
effect of the domain knowledge of the information sender.
This research has certain novelties and contributions. From a theoretical perspective, firstly, this
research proposes and validates the lay belief that “ingredient = professional” based on the frequent
co-occurrence of ingredient and professional-related concepts, which is a new and unexplored lay
belief and a major innovation of this research. Secondly, this research is the first research to explore
how recommendation terms (ingredient vs. function) influence consumer attitudes towards functional
products in an online word-of-mouth context. This is another innovative point of this research and
contributes to the literature on factors influencing online word-of-mouth persuasiveness. Thirdly,
this research identifies an important boundary condition for recommendation terms to work, the
domain knowledge of the information sender, which advances the literature on online word-of-mouth
and domain knowledge. From a practical perspective, this research provides marketers with viable
practical guidance on how product ingredients should be properly used to amplify functional products
in online word-of-mouth recommendations.
Consumers are often inclined to buy products recommended by others in the buying process in
order to make efficient and rational decisions (Smith et al., 2005). Word-of-mouth recommendation,
Volume 31 • Issue 2
3
in which people communicate information about a product, brand, or service to others for non-
commercial purposes (Arndt, 1967), is an important source of information for consumers to receive
recommendations. Word-of-mouth plays a significant role in determining consumer attitudes and
purchase decisions since it is a kind of interpersonal communication rather than advertising (Moon
et al., 2017). With the advent of Internet commerce, the rise of online platforms such as social media
(e.g., Facebook, Twitter, Weibo, and Xiaohongshu), online shopping platforms (e.g., Amazon, Taobao),
and question-and-answer online communities (e.g., Zhihu and CSDN; Metzger & Flanagin, 2013),
word-of-mouth recommendation has shifted from traditional word-of-mouth to online communication
(Hennig-Thurau & Walsh, 2003). Online word-of-mouth refers to comments made either favorably or
unfavorably about a product and disseminated via online media (Thorson & Rodgers, 2006). These
online platforms facilitate consumer discussions about products and brands (Wolny & Mueller, 2013),
and these online platforms also facilitate brands, influencers (e.g., opinion leaders), and consumers
to distribute product-related content (Barreda et al., 2015), that have been found to affect consumer
purchasing decisions significantly (Schlosser, 2011; Wang et al., 2012).
Existing research has focused on two dimensions of online word-of-mouth recommendations:
recommendation systems and recommended content (e.g., Resnick & Varian, 1997; Ma & Ding, 2018).
On the one hand, some research has focused on recommender systems, i.e., learning from large-scale
population data to achieve user group feature matching, thus helping users to effectively identify
content of interest from large amounts of data (Resnick & Varian, 1997). In reality, recommendation
systems are still widely used in applications other than online word-of-mouth recommendations,
including ordering tools, self-replenishment technologies, automatic meal planning, online social
networks, etc. Specifically, Ramadan et al. (2019) found that Amazon Prime Now, an online ordering
tool that promises same-day delivery, increased consumer satisfaction and led to a trusting relationship
between consumers and retailers. Farah et al. (2020) discussed the impact of the introduction of self-
replenishment technology devices on consumer behavior. The findings suggest that self-replenishment
technology will not replace traditional shopping media, but its use depends on consumer brand loyalty
and overall consumer lifestyles. Salloum and Tekli (2022) introduced an automatically generated
meal plan recommendation called MPG, which can take into account both calorie intake and patient
preferences such as food preferences, food variety, and compatibility between foods. Finally, Arafeh
et al. (2020) proposed a new sampling framework for online social network data that can be used to
help build recommendation systems based on ontology.
On the other hand, the part of the research that is more relevant to consumer behavior focuses
on recommendation content, i.e., the specific content of word-of-mouth recommendation messages,
including text, images, and videos (Ma & Ding, 2018; Fan et al., 2021). Research shows that
the characteristics of word-of-mouth recommended content, including emotion, interestingness,
professionalism, reliability, richness, quality, and relevance, all have an impact on the attitude or
behavior of information recipients (e.g., Brodie et al., 2013; Yin et al., 2014; Wakefield & Bennett,
2018). Indeed, the essence of a recommendation system is an information filtering system, and the
recommended content is the important factor that influences the final decision of the consumer.
Based on the Source of the Information
Consumer trust determines how online word-of-mouth recommendation affects their attitudes and
purchasing intention. Research suggests that information receivers judge the trustworthiness of online
word-of-mouth recommendations by the source and content of the information (Rieh & Danielson,
2007; Choi & Rifon, 2002). Trustworthiness based on the source of information is more influential to
the receiver than the content of the information (Phua et al., 2018). Source credibility can be defined
as credibility based on the expertness and trustworthiness of the information sender (Hovland &
Volume 31 • Issue 2
4
Weiss, 1951). Professionalism refers to the extent to which the communicator is perceived to be able to
provide valid and accurate information, and trustworthiness refers to the extent to which the recipient
perceives the communicator to be willing to provide the truth (Hovland & Weiss, 1951). Source
credibility plays an important role in the persuasion process, and the higher the source credibility, the
more likely the recipient is to be persuaded, given the same message content (Hovland & Weiss, 1951;
Rieh & Danielson, 2007). Certain aspects of online word-of-mouth, such as source type (Hilligoss &
Rieh, 2008), sponsor disclosure (Hwang & Jeong, 2016), characteristics of the sender (Nikolinakou &
Phua, 2020), and expertise of the sender (Wang & Scheinbaum, 2018) will affect source credibility.
Based on the Content of the Information
In fact, online word-of-mouth is different from traditional word-of-mouth, which usually occurs
between familiar individuals, while online word-of-mouth occurs between individuals who have
no common connection (Metzger & Flanagin, 2013). As a result, in the context of online word-of-
mouth, information recipients frequently struggle to comprehend the knowledge and expertise of the
information sender, making it challenging to rely on the source’s credibility to determine whether the
word-of-mouth information is reliable or not. Therefore, information recipients tend to rely more on
the cues in the information content to make their judgment (Wang, 2001). Signal theory can account
for this cue-based behavior of trust judgments. The main principle of signal theory is that buyers can
use information provided by sellers as signals to infer the validity of seller and seller states (Kirmani
& Rao, 2000). Information senders might purposefully offer certain information signals, including
social or content characteristics, in the context of online word-of-mouth in to inf luence consumer
trust, purchase intentions, and product evaluations (Cheung et al., 2014).
Previous studies distinguished functional products from hedonic products (Batra & Ahtola, 1991).
Functional products refer to products that can meet specific consumer tasks or necessities of life,
such as skin care products and hard drives (Botti & Mcgill, 2011); while hedonic products refer to
products that can meet consumers’ inner emotional needs, such as snacks and entertainment films
(Salerno et al., 2014). In terms of decision evaluation and purchase motivation, the two categories of
products differ from one another. The consumer interests in purchasing functional products depend
on whether they can complete specific tasks, and consumers will be satisfied with products that meet
or exceed their functional requirements (Chitturi et al., 2008; Baltas et al., 2017). Therefore, when
consumers choose functional products, their decision-making behaviors will be more rational, and
they will pay more attention to dimensions such as product function and value (Ran & Zheng, 2017).
In contrast, consumer interest in purchasing hedonic products stems from the pleasure generated
during the shopping experience, and consumers are willing to seek experiences of happiness and
enjoyment (Moore, 2015; Yang et al., 2016). Therefore, when consumers choose hedonic products,
their decision-making behaviors will be more perceptual, and they will pay more attention to the
appearance, attractiveness, and pleasantness of products (Yim et al., 2014). However, few studies
focus on how to better convey the function and value of functional products in the context of online
word-of-mouth.
According to research, emphasizing product ingredients can better convey the benefits of functional
productions. Product ingredients are increasingly used in marketing communications, and marketers
often describe the specific ingredients and research and development production process of products
on promotional slogans, product packaging, and brand websites (Lellis, 2016). Especially in the field
of food health, consumers are generally interested in product ingredients while purchasing products
(Grunert & Wills, 2007). In the category of functional products, consumers are also paying more
and more attention to the ingredients of cosmetics, and consumers will prefer cosmetics that do not
Volume 31 • Issue 2
5
contain certain ingredients (Xu & Wyer, 2010). For example, consumers will deliberately avoid
preservatives such as parabens when shopping for cosmetics. As a result, companies using such
preservatives are gradually changing their product ingredients and even removing cosmetics containing
such preservatives from shelves (Xu & Wyer, 2010). It is clear that emphasizing the ingredients of
functional products can be particularly important in the context of online word-of-mouth.
In order to understand consumers’ attitudes towards the use of product ingredients in online word-
of-mouth, we explored consumers’ lay beliefs about product ingredients. Lay Beliefs are common
sense explanations that people have about themselves and the outside world (Molden & Dweck,
2006; Schwarz, 2004), such as “beauty = good” (Wan et al., 2017), “ethical products = less strong”
(Mai et al., 2019), “distraction = interest” (Zane et al., 2020), “multiple payment channels =
commercialization” (Ran et al., 2021), “the scientific process = cold” (Avivia et al., 2022). When
information is incomplete or the source credibility is low, individuals often form mental perceptions
from accessible subtle cues and rely on lay beliefs to form meaningful inferences about these mental
perceptions, which influence their perceptions and attitudes (Schwarz, 2004; Zane et al., 2020). For
example, Zane et al. (2020) found that when a stimulus distracted people from a task, they inferred
that the stimulus was interesting based on the lay belief that “distraction = interest,” which led to a
positive evaluation. In the field of consumer behavior, previous research has demonstrated that lay
beliefs can have an impact on consumers’ judgments about various products (Cheng et al., 2017;
Deval et al., 2013; Wang et al., 2010), even if these beliefs are not necessarily objectively accurate
(Haws et al., 2017; Kramer & Block, 2011).
We propose that in online word-of-mouth contexts, consumers rely on the lay belief that
“ingredient = professional.” Labeling product ingredients makes consumers perceive the content of
word-of-mouth messages as more professional than directly labeling product functions. Lay beliefs
are mainly derived from personal experiences and observations of the outside world (Kyung et al.,
2017; Mai et al., 2019). Individuals gradually build a relationship between two occurrences in life
that regularly happen together, leading to the formation of lay beliefs (Zane et al., 2020). People with
extensive domain knowledge typically use chemical names when expressing very specialized content
because product ingredients are typically very complex chemical names. Therefore, in online word-
of-mouth contexts, consumers may hold the lay belief that “ingredient = professional,” as ingredients
are frequently associated with professional-related concepts.
In the online word-of-mouth context, for functional products, the recommendation terms used by
the information sender (ingredient vs. function) can impact consumers’ perceived trustworthiness
and thus influence purchase intentions and product evaluations. Unlike traditional word-of-mouth
recommendations, which are often sourced from close acquaintances, online word-of-mouth
recommendations often come from anonymous senders. It is difficult for consumers to rely on the
source’s credibility to determine whether the recommendation is trustworthy (Park & Lee, 2009).
According to signal theory, when the source credibility is low, the buyer will use the characteristics
of the content of the information provided by the seller as a signal to infer whether the information is
trustworthy (Kirmani & Rao, 2000; Dimoka et al., 2012). Therefore, when consumers are approached
with online word-of-mouth recommendations, they pay particular attention to the content of the
recommendation information, especially the recommendation terms used by the recommender, and
use the recommendation terms as signals to infer the authenticity and validity of the information.
On the basis of the lay belief that “ingredient = professional,” customers will perceive ingredient
labeling for functional products to be significantly more professional than direct function labeling.
Additionally, consumers give significant weight to the functionality and functional expertise of the
Volume 31 • Issue 2
6
products when making purchase decisions since their interest in purchasing functional products
depends on the products’ capacity to meet specific task needs. (Ran & Zheng, 2017; Baltas et al.,
2017). Therefore, we believe that the professionalism of the recommendation information will enhance
the perceived trustworthiness of the sender and the content of the message. It is helpful for consumers
to believe that the content of online word-of-mouth recommendations is accurate and trustworthy,
thereby improving purchase intention and product evaluation. In summary, this research puts forward
the following hypotheses:
Hypothesis 1 (H1): In online word-of-mouth contexts, for functional products, labeling ingredients
leads to higher purchase intentions and product evaluations by consumers compared to labeling
functions.
Hypothesis 2 (H2): Perceived trustworthiness mediates the effect of recommendation terms (ingredient
vs. function) on purchase intention and product evaluation.
The influence of recommended terms used by the information sender on consumers’ purchase intention
is moderated by the domain knowledge of the information sender. On the one hand, research on trust
demonstrates that in the context of online word-of-mouth, source credibility has a stronger influence
on the information recipient than assessments based on trust cues (Phua et al., 2018). Therefore,
regardless of whether the recommendation terms used by the information sender are ingredients or
functions, consumers will directly judge the source to be more trustworthy based on that domain
knowledge and thus have a higher purchase intention when the information sender has a high level
of domain knowledge for a functional product (Hovland & Weiss, 1951). On the other hand, research
on lay beliefs shows that information persuasiveness decreases when its attributes conflict with lay
beliefs held by consumers (Haws et al., 2017). Therefore, when the information sender has low domain
knowledge, compared to the labeling function, consumers will perceive a mismatch between the low
domain knowledge owned by the information sender and the high domain knowledge required for the
labeling ingredient, resulting in lower purchase intentions and product evaluations. Based on this,
this research proposes the following hypothesis:
Hypothesis 3 (H3): Information senders’ domain knowledge moderated the effect of recommended
terms on purchase intention and product evaluation. There was no significant difference in
purchase intention and product evaluation between labeled ingredients and labeled functions
when information senders had high domain knowledge, while labeled functions had higher
purchase intention and product evaluation than labeled ingredients when information senders
had low domain knowledge.
These hypotheses were tested across five studies (one pilot study and four laboratory experiments).
The pilot study utilized two implicit measurement methods (object matching task and word
association task) to verify the existence of lay beliefs in consumers’ minds that “ingredient =
professional.” Study 1 validated the main hypothesis of this research that labeled ingredients
can trigger higher individual purchase intention than labeled functions in online word-of-mouth
recommendations (i.e., H1). Study 2 verified the mediating role of perceived trustworthiness (i.e.,
H2). Study 3 extended the effect of recommendation terms from purchase intention to product
evaluation, examining the effect of recommendation terms on purchase intention and product
evaluation, respectively, and revalidating the mediating mechanism of perceived trustworthiness.
Lastly, Study 4 examined the moderating effect of the domain knowledge of the information
sender (i.e., H3). That is, there was no difference in purchase intention and product evaluation
Volume 31 • Issue 2
7
between labeled ingredients and labeled functions when the information sender had high domain
knowledge, while labeled functions had higher purchase intention and product evaluation than
labeled ingredients when the information sender had low domain knowledge. The empirical model
is presented in Figure 1, and we summarize these findings in Table 1.
Figure 1. The empirical model
Table 1. Overview of the studies
Study Stimuli Sample
Size
Moderator Mediator Covariates DV Main Findings
Ingredient
terms
(SE)
Function
terms
(SE)
Planned
Contrast
Pilot
Study
— 100 — — — — — — —
Study
1
Lifting and
firming
cream
200 — — Frequency
of use
Purchase
intention
5.24 (.11) 4.87
(.11)
F (1, 197)
= 5.19, p
= 0.024
Study
2
Acne
essence
122 — Perceived
trustworthiness
Frequency of
purchase
Purchase
intention
5.16(.16) 4.62
(.16)
F (1, 118)
= 5.13, p
= 0.025
Study
3
Lifting and
firming
cream
200 — Perceived
trustworthiness
— Purchase
intention
5.52 (.13) 5.07
(.13)
F (1, 198)
= 5.64, p
= 0.018
Product
evaluation
7.18 (.14) 6.75
(.14)
F (1, 198)
= 5.08, p
= 0.025
Study
4
Anti-aging
supplements
397 Information
sender’s
domain
knowledge
(high vs.
low)
— Participants’
domain
knowledge;
brushing
frequency;
professionalism
of ingredients
Purchase
intention
4.86 (.31)
4.12 (.12)
4.83
(.12)
4.69
(.12)
F (1, 390)
< 1, p =
0.877
F (1, 390)
= 11.47,
p = 0.001
Product
evaluation
6.60 (.13)
5.81 (.13)
6.51
(.13)
6.27
(.13)
F (1, 390)
< 1, p =
0.630
F (1, 390)
= 6.37, p
= 0.012
Volume 31 • Issue 2
8
The pilot study verified that the lay belief of “ingredient = professional” is widely existing in the minds
of consumers, which supports the premise logic of this research. Specifically, we use two implicit
measurement methods, the object matching task and the word association task, to measure the lay
belief of “ingredient = professional” (Ran et al., 2021). First, the process of the object matching task
was to let the participant imagine a certain situation, and then let him choose the object that matches
the situation. Because individuals process stimuli that match lay beliefs more quickly and feel more
reasonable (Higgins, 1996; Mai et al., 2019). Second, the process of the vocabulary association task
was first to show the participant the target vocabulary, and then ask which words to choose that are
more likely to be associated with the target word. We predicted that labeling ingredients will be
perceived as better matched to professional objects and associated with more words related to the
concept of “professional” than the labeling function.
The pilot study adopted a single factor (recommended term: ingredient vs. function) between-subject
design experiment. We recruited 100 participants, divided into two groups of 50 each, to participate in
this online experiment through the data mart on the Credamo platform (a general online subject pool).
Of these, 53.0% were female and the age range was 18 to 55 years (M = 27.69, SD = 6.59). Upon
entering the experiment, participants are assigned to perform the following two tasks in sequence.
The Vocabulary-Website Type Matching Task
Participants were first told that the next set of words they saw were real words that appeared
on a website. Participants in the ingredient condition saw the words “10-hydroxydecanoic acid,
mucopolysaccharide polysulfate, hydroxy cumene glycosides” and those in the function condition
saw the words “fast acne removal, pore unblocking, acne mark reduction.” The participants were
then asked to guess the most likely and least likely websites for this set of terms from a controlled
selection of four websites. There were four websites to choose from: the official website of the Journal
of Chemistry, the official website of Ruili Fashion and Beauty Magazine, the official website of the
Chinese Journal of Integrative Medicine, and the official website of Fashion Bazaar. Among them,
the official websites of the Journal of Chemistry and the Chinese Journal of Integrative Medicine
are academic journals, which are typically highly specialized websites, while the official websites
of Ruili Fashion and Beauty Magazine and Fashion Bazaar are typically less specialized websites.
The Vocabulary Association Task
First, participants were presented with 3 target words and 16 alternative words. Afterward, participants
were asked to select the four words that were most closely associated with the target vocabulary
out of the 16 alternatives. In this task, both conditions of participants saw the same target words as
in the website type matching task. The 16 alternatives included 8 words related to the concept of
professional (drill, complex, proficient, thesis, professor, insider, doctor, scalpel) and 8 words related
to the concept of non-professional (browsing, simple, slight knowledge, magazine, enthusiast, amateur,
make-up artist, eyebrow trimmer), which corresponded to each of the two groups of words. Finally,
participants reported demographic information such as gender and age and made guesses about the
purpose of the experiment.
The descriptive results of the pilot study are shown in Table 2.
Volume 31 • Issue 2
9
Vocabulary-Website Type Matching Task Results
The results of the chi-square test showed that participants generally perceived that ingredient words
(44/50, 88.0%) were more likely to be found on more professionally requested sites than function
words (13/50, 26.0%), χ2 (1) = 39.21, p < 0.001, φ = -0.63, lower than -0.50, with a larger effect
size (Cohen, 1988). At the same time, participants generally perceived that ingredient words (5/50,
10.0%) were less likely to appear on less professionally requested sites than function words (39/50,
78.0%), χ2 (1) = 46.92, p < 0.001, φ = 0.68, with a larger effect size.
Vocabulary Association Task Results
We marked words related to the concept of specialization as 1 and words related to the concept of non-
specialization as -1, and the total score of the participants’ vocabulary choices was used as an indicator
of specialization. The higher the score, the higher the degree of association with specialization. A
single-factor ANOVA was conducted with the specialization indicator as the dependent variable. The
results showed that participants were more likely to associate the ingredient vocabulary (Mingredient =
2.92, SD = 1.52) with words related to the concept of specialization than the function vocabulary
(Mfunction = -0.12, SD = 2.50), F (1, 98) = 53.79, p < 0.001, η2 = 0.35. The effect size was larger
according to Cohen’s (1988) higher effect size criterion of 0.14.
The pilot study used two measures to confirm the lay belief that “ingredient = professional,” supporting
the logical premise of this research, and Study 1 will verify the effect of recommendation terms on
consumer purchase intention.
Study 1 aimed to test the basic hypothesis of this research, namely that labeled ingredients can
trigger higher purchase intention among individuals than labeled functions in online word-of-mouth
recommendations (i.e., H1).
Study 1 adopted a single factor (recommended term: ingredient vs. function) between-subjects design
experiment. We recruited 200 participants, divided into two conditions of 100 each, to participate
in the online experiment through the data mart on the Credamo platform. Each participant was paid
in cash at the end of the experiment. Of these, 147 (73.5%) were women, aged 19 to 51 years (M =
30.04, SD = 8.75).
First, the participant was asked to imagine that she was browsing Xiaohongshu, a widely used
online word-of-mouth recommendation platform in China, when she came across a blog post (see
Table 2. Pilot study descriptive results
Conditions What Site Is It Most Likely to
Appear On?
What Site Is It Least Likely to
Appear On?
Vocabulary
Professionalism Related to
Ingredients (vs. Functions)
Recommended Terms
More professionally
requested sites
More
professionally
requested sites
Less
professionally
requested sites
More
professionally
requested sites
Less
professionally
requested sites
Ingredients 88.0% 12.0% 90.0% 10.0% 2.92 (1.52)
Functions 26.0% 74.0% 22.0% 78.0% -0.12 (2.50)
Volume 31 • Issue 2
10
Web Appendix 1) by a blogger recommending a lifting and firming face cream. In the condition
of the ingredient term, the blog post is illustrated with the group of ingredients “oligopeptide-2,
acetyl hexapeptide-8, palmitoyl tetrapeptide-7.” In the condition of the functional term, the article
is illustrated with the group of functions “anti-aging, reduces the appearance of lines, lifts the
jawline.” Participants’ purchase intentions (1 = would not, 7 = would) were then measured: “Would
you purchase this product?” (Verhagen & Dolen, 2009). In addition, to control for the effect of
the experience of use, we measured the frequency of use of the product by the participants: “How
often do you usually use anti-aging supplements?” (1 = never used, 7 = used regularly). Finally,
participants filled in demographic information such as gender and age and made guesses about the
purpose of the experiment.
A one-way ANCOVA was conducted with purchase intention as the dependent variable and frequency
of use as the control variable. The results showed that participants who saw the ingredient term
(Mingredient = 5.24, SE = 0.11) were more likely to purchase the recommended product compared to
those who saw the functional term (Mfunction = 4.87, SE = 0.11), F (1, 197) = 5.19, p = 0.024, η2 =
0.03, supporting H1.
Study 1, utilizing a lifting and firming face cream as the experimental material, verified that participants
were more likely to purchase the recommended product when presented with the ingredient term
compared to the functional term, providing support for the underlying hypothesis of this research.
Next, Study 2 explored the mediating mechanism of the focal effect.
Study 2 replicated our main effect (i.e., H1) that participants were more likely to purchase the
recommended product when presented with the ingredient term compared to the functional term.
More importantly, this study examined the possible mediating effect of perceived trustworthiness as
we have proposed (i.e., H2).
Study 2 adopted a single factor (recommended term: ingredient vs. function) between-subjects design
experiment. We recruited 121 participants, divided into two conditions, to participate in the online
experiment through the data mart on the Credamo platform. Each participant was paid in cash at the
end of the experiment. Of these, 81 (66.9%) were women, aged 18 to 56 years (M = 29.45, SD = 7.83).
The procedure was similar to that of Study 1. The focal product was changed into an acne
serum (see Web Appendix 2 for pictures presented). The study began with the manipulation
of recommendation terms. The participant was asked to imagine that he or she was browsing
Xiaohongshu and saw a blog post by a blogger recommending an acne serum. In the context of the
ingredient term, the blog post was illustrated with a set of ingredients labeled “mucopolysaccharide
polysulfate, 10-hydroxydecanoic acid, hydroxy cumene glycosides.” In the condition of the
functional term, the article is illustrated with a set of functions “fast acne relief, unblocking
pores, reducing acne marks.” Participants’ purchase intentions (1 = unwilling/would not/unlikely/
improbable, 7 = willing/would/likely/probable; r = 0.78) were then measured (“Would you
purchase this product?”; Verhagen & Dolen, 2009), as well as perceived trustworthiness (“I
think the information conveyed in the above blog post is trustworthy,” 1 = strongly disagree, 7 =
strongly agree; Lee et al., 2010). In addition, the frequency of participants’ purchases of this type
of product was measured: “How often do you usually buy acne products?” (1 = never bought, 7 =
Volume 31 • Issue 2
11
often bought). Finally, participants filled in demographic information such as gender and age and
made guesses about the purpose of the study.
Purchase Intention
A one-way ANCOVA was conducted with purchase intention as the dependent variable and frequency
of purchase as the control variable. The results showed that participants who saw the ingredient term
(Mingredient = 5.18, SE = 0.16) were more likely to purchase the recommended product compared to
those who saw the functional term (Mfunction = 4.66, SE = 0.16), F (1, 118) = 5.13, p = 0.025, η2 =
0.04, supporting H1.
Perceived Trustworthiness
A one-way ANCOVA was conducted on perceived trustworthiness, again using the frequency of
purchase as the control variable. The results showed that participants perceived the information
conveyed by blog posts labeled with ingredient terms (Mingredient = 4.78, SE = 0.17) to be more
trustworthy than those labeled with functional terms (Mfunction = 4.07, SE = 0.17), F (1, 118) = 8.73,
p = 0.004, η2 = 0.07.
The Mediating Role of Perceived Trustworthiness
This study examined the mediating role of perceived trustworthiness using the mediation analysis
model of Preacher and Hayes (2008; Model 4, bootstrapping 5,000 times). The results show that
perceived trustworthiness significantly mediate the effect of recommendation terms on purchase
intention (indirect effect = -0.494, SE = 0.169, 95% CI: [-0.817, -0.147]), supporting H2.
Study 2 replicated the effect of recommendation terms on consumer purchase intentions (i.e., H1)
and verified the mediating role played by perceived trustworthiness in the recommendation term
effect (i.e., H2). More specifically, labeling ingredients can increase the perceived trustworthiness
of recommendations and, thus, product purchase intention, compared to labeling functions in word-
of-mouth recommendations.
Study 3 extended the effect of recommendation terms from purchase intention to product evaluation,
examining the effect of recommendation terms on purchase intention and product evaluation,
respectively, and re-validating the mediation mechanism of perceived trustworthiness.
Study 3 adopted a single factor (recommended term: ingredient vs. function) between-subjects design
experiment. We recruited 200 participants, divided into two groups of 100 each, to participate in
the online experiment through the data marketplace on the Credamo platform. Each participant was
paid in cash at the end of the experiment. Of these, 139 (69.5%) were women, aged 19 to 51 years
(M = 29.75, SD = 6.60).
The procedure was similar to that of Study 1. First, the participant was asked to imagine browsing
Xiaohongshu, a widely used word-of-mouth recommendation platform in China, and coming across
a blog post (see Web Appendix 3) recommending a lifting and firming face cream. In the condition
of the ingredient term, the blog post included the group of ingredients “oligopeptide-2, acetyl
hexapeptide-8, palmitoyl tetrapeptide-7.” In the context of the functional term, the article included
Volume 31 • Issue 2
12
the group of functions “anti-aging, fades lines and wrinkles, lifts the jawline.” Next, participants
responded with a product evaluation (1 = bad/negative/dislike it very much, 9 = good/positive/like it
very much; α = 0.90; Huang et al., 2013) and a purchase intention (1 = unwilling/would not/unlikely/
improbable, 7 = willing/would/likely/probable; α = 0.94; Verhagen & Dolen, 2009). Afterward,
perceived trustworthiness was measured (“I think the information conveyed in the above blog post
is professional/trustworthy,” 1 = strongly disagree, 7 = strongly agree; Lee et al., 2010). Finally,
participants filled in demographic information such as gender and age and guessed the purpose of
the experiment.
Purchase Intention
One-way ANOVA results showed that participants who saw the ingredient terms (Mingredient = 5.52,
SE = 0.13) were more likely to purchase the recommended product compared to those who saw the
function terms (Mfunction = 5.07, SE = 0.13), F (1, 198) = 5.64, p = 0.018, η2 = 0.03, again supporting H1.
Product Evaluation
Again, a one-way ANOVA was conducted on product evaluations, and the results showed that
participants who saw the ingredient terms (Mingredient = 7.18, SE = 0.14) rated the recommended
product more highly compared to those who saw the function terms (Mfunction = 6.75, SE = 0.14), F
(1, 198) = 5.08, p = 0.025, η2 = 0.03.
Perceived Trustworthiness
A one-way ANOVA on perceived trustworthiness showed that participants perceived blog posts
labeled with ingredient terms (Mingredient = 5.34, SE = 0.13) as more trustworthy than those labeled with
function terms (Mfunction = 4.82, SE = 0.13), F (1, 198) = 7.97, p = 0.005, η2 = 0.04 (see Figure 2).
The Mediating Role of Perceived Trustworthiness
This study examined the mediating role of perceived trustworthiness using the Preacher and Hayes
(2008) mediation analysis Model 4 (bootstrapping 5,000 times) with purchase intention and product
Figure 2. Effects of recommendation terms on purchase intention, product evaluation, and perceived trustworthiness
Volume 31 • Issue 2
13
evaluation as dependent variables, respectively. The results showed that perceived trustworthiness
mediated significantly in the effect of recommendation terms on purchase intention (indirect effect =
-0.438, SE = 0.163, 95% CI: [-0.774,-0.134]) and in the effect of recommendation terms on product
evaluation (indirect effect = -0.432, SE = 0.155, 95% CI: [-0.738,-0.134]), again offering support
for H2.
Study 3 verified the effect of recommendation terms on purchase intentions and product evaluations,
and again verified the mediating role of perceived trustworthiness, enhancing the external validity
of the study.
Study 4 was designed to affirm the moderating effect of domain knowledge (i.e., H3). That is, there
was no difference in purchase intention and product evaluation between labeled ingredients and
labeled functions when the information sender had high domain knowledge, while labeled functions
had higher purchase intention and product evaluation than labeled ingredients when the Information
sender had low domain knowledge.
A total of 400 participants were recruited from a general population subject pool via Credamo. This
study adopted a 2 × 2 (recommended term: ingredient vs. function; domain knowledge: high vs.
low) between-subjects design. Three participants not completing the attention check were excluded
from data analysis (Oppenheimer et al., 2009), resulting in 397 valid data (Mage = 29.82, SD = 7.29;
68.8% female).
Similar to the previous experiment, participants were first asked to imagine seeing a blog post
(see Web Appendix 4) recommending an anti-aging supplement while browsing Xiaohongshu.
For the manipulation of recommendation terms, in the condition of the ingredient terms, the blog
post illustrated that the product contained the group of ingredients “resveratrol, β-nicotinamide
mononucleotide, pyrroloquinoline quinone.” In the context of function terms, the product is illustrated
as having “anti-thrombotic, anti-aging, and free radical scavenging” functions. For the manipulation of
domain knowledge, in the high domain knowledge scenario, participants were informed that the product
recommendation was published by a health professional blogger with extensive health expertise. In
the low domain knowledge scenario, the participants were told that the product recommendations
were posted by a blogger who was a novice in health care and had little health care expertise.
Next, purchase intention and product evaluation were measured using the same questions as
in Study 3. Afterward, participants were asked about their own domain knowledge (“Do you know
anything about anti-aging supplements?” 1 = very unaware, 7 = very aware), Xiaohongshu browsing
habits (“How often do you usually browse Xiaohongshu?” 1 = never, 7 = often), and the professionality
of labeling ingredients (“Do you think labeling the product features in the blog post would make the
post more professional?” 1 = very unprofessional, 7 = very professional). Finally, participants filled
in demographic information such as gender and age and guessed the purpose of the experiment.
Purchase Intention
A two-way ANOVA was conducted with purchase intention as the dependent variable and the
participants’ own domain knowledge, frequency of browsing Xiaohongshu, and professionalism of
labeled ingredients as control variables. The results showed that the main effect of recommendation
terms was significant (F (1, 390) = 5.29, p = 0.022), and the main effect of domain knowledge was
Volume 31 • Issue 2
14
significant (F (1, 390) = 12.76, p < 0.001). Significantly, the interaction effect of recommendation
terms and domain knowledge was significant (F (1, 390) = 6.31, p = 0.012). Planned contrasts
showed that no significant differences in purchase intention were found between ingredient terms
and function terms when information senders had high domain knowledge (Mingredient = 4.86, SE =
0.12, Mfunction = 4.83, SE = 0.12; F (1, 390) < 1, p = 0.877, ηp
2 < 0.001; see Figure 3). However, when
information senders had lower domain knowledge, participants who saw function terms had higher
purchase intentions compared to ingredient terms (Mingredient = 4.12, SE = 0.12, Mfunction = 4.69, SE =
0.12; F (1, 390) = 11.47, p = 0.001, ηp
2 = 0.03), supporting H3.
Product Evaluation
Similar two-way ANOVA results showed a non-significant main effect for recommendation terms (F
(1, 390) = 2.12, p = 0.146) and a significant main effect for domain knowledge (F (1, 390) = 15.20,
p < 0.001). Significantly, there was a significant interaction effect between recommended terms and
domain knowledge (F (1, 390) = 4.54, p = 0.034). Simple effects analysis showed (see Figure 4) that
no significant differences were found between product evaluation for ingredient terms and function
terms when the Information sender had high domain knowledge (Mingredient = 6.60, SE = 0.13, Mfunction
= 6.51, SE = 0.13; F (1, 390) < 1, p = 0.630, ηp
2 = 0.001). However, when the Information sender
had lower domain knowledge, participants who saw the function term gave higher product evaluation
compared to the ingredient term (Ming redient = 5.81, SE = 0.13, Mfunction = 6.27, SE = 0.13; F (1, 390)
= 6.37, p = 0.012, ηp
2 = 0.02).
The moderating effect of domain knowledge was verified in Study 4, using health products as stimuli.
Specifically, when the Information sender’s domain knowledge was high, there was no difference
between labeled ingredients and functions, while when the information sender’s domain knowledge
was low, labeled functions were more likely to be purchased and rated as a product than labeled
ingredients.
Figure 3. Moderating effect of domain knowledge on the effect of recommended terms on purchase intention
Volume 31 • Issue 2
15
This research investigated the effect of recommendation terms (ingredient vs. function) on consumer
purchase intention and product evaluation, as well as their underlying mechanisms and boundary
conditions, in the context of online word-of-mouth recommendations for functional products, based on
the lay belief that “ingredient = professional.” Through one pilot study and four laboratory experiments,
it was found that: First, the lay belief that “ingredient = professional” is widely present in the minds of
consumers (pilot study). Secondly, the purchase intention (Studies 1–3) and product evaluation (Study
3) of recommended products with labeled ingredients were higher for consumers compared to labeling
functions in online word-of-mouth recommendations. Then, perceived trustworthiness mediated the
effect of recommendation terms on purchase intention (Studies 2–3) and product evaluation (Study 3).
Finally, the domain knowledge of the information sender moderated the effect of the recommendation
terms on consumer purchase intention and product evaluation. When the domain knowledge of the
information sender was high, there was no difference in the effect of labeled ingredients and labeled
functions on consumer purchase intention and product evaluation, while when the domain knowledge
of the information sender was low, labeled functions triggered higher purchase intentions and product
evaluations than labeled ingredients (Study 4).
This research contributes to the development of multifaceted literature in the field of psychology
and marketing. Firstly, this research proposes and validates a new lay belief that “ingredient =
professional,” which is a major novelty of this research and broadens the scope of research on lay
beliefs. In previous psychological research, scholars have explored the role of lay beliefs, such as
“beauty = good” (Wan et al., 2017) and “scientific processes = cold” (Aviva et al., 2022). It is
believed that it will affect consumer product evaluation, purchase intention, donation intention, and
other aspects. However, no research has examined the lay beliefs related of consumers in relation to
product ingredients. This research explored the use of lay beliefs in the field of online word-of-mouth
and proposes that consumers will hold the lay belief of “ingredient = professional,” which influences
consumer attitudes and behaviors.
Figure 4. Moderating effect of domain knowledge on the effect of recommended terms on product evaluation
Volume 31 • Issue 2
16
Secondly, this research broadens the scope of considerations that influence consumer perceived
trustworthiness of online word-of-mouth. In previous studies, scholars have often focused on
online word-of-mouth source credibility, that is, judging the credibility of information based on
the professionalism and trustworthiness of the disseminator (Hovland & Weiss, 1951), and have
focused primarily on the factors that affect source credibility. For example, source type (Hilligoss &
Rieh, 2008), sponsor disclosure (Hwang & Jeong, 2016), and the expertise of the sender (Wang &
Scheinbaum, 2018). This affects the level of consumer trust in online word-of-mouth. It is worth noting
that, unlike traditional word-of-mouth recommendations, which often come from close acquaintances,
online word-of-mouth recommendations often come from anonymous senders, so it is difficult for
consumers to rely on source credibility to determine whether the recommendation is trustworthy (Park
& Lee, 2009). However, few studies have examined how consumers make trust judgments based on
subtle cues when they cannot directly judge the trustworthiness of a source. Drawing on signal theory
(Dimoka et al., 2012) and lay beliefs (Aviva et al., 2022; Ran et al., 2021), this research suggests that
labeling product ingredients in online word-of-mouth is an important factor influencing consumer
attitudes, this is another novelty of this research.
Finally, this research identifies the boundary conditions under which consumer psychology is
influenced by recommended terms. This research argues that the influence of the recommendation
terms used by the information sender on consumer attitudes is moderated by the domain knowledge
of the information sender. On the one hand, based on research on trust, in online word-of-mouth
contexts, source credibility has a greater impact on information recipients than judgments formed
based on trust cues (Phua et al., 2018). We suggest that consumers directly judge a source as more
trustworthy based on domain knowledge when the information sender has higher domain knowledge,
and thus has higher purchase intentions, independent of the recommendation terms. On the other hand,
based on research on lay beliefs, the persuasive power of a message decreases when the attributes
of the message conflict with the lay beliefs held by the consumer (Haws et al., 2017). This research
considers that when the information sender has low domain knowledge, a mismatch between the low
domain knowledge possessed by the information sender and the high domain knowledge required to
label ingredients rather than functions in the information content is perceived by consumers, leading
to a reversal of the effect of the recommended terms.
This research provides practical insights for companies and brands to conduct word-of-mouth
marketing effectively. The results of this article show that for functional products, consumer purchase
intentions and product evaluations are increased for recommended products with ingredients than for
those with features in online word-of-mouth recommendations. Based on the lay belief that “ingredient
= professional,” the use of ingredients as a recommendation term for functional products provides the
consumer with a higher perception of professionalism, and word-of-mouth recommendation content
is more trusted by consumers. Therefore, when businesses and brands want to promote functional
products through online word-of-mouth marketing, they may think about including the ingredients
in their recommendation messages.
However, businesses and brands need to pay close attention to the information sender’s domain
knowledge when using ingredients for online word-of-mouth recommendations. This is due to
the fact that the recommended terms’ effects are moderated by the information sender’s domain
knowledge. Since source credibility has a greater impact on information receivers than judgments
formed based on trust cues (Phua et al., 2018), when businesses or brands choose online word-of-
mouth recommendations for marketing and promotion, in addition to selecting opinion leaders with
a high level of domain knowledge, they can use a more practical method of maximizing the effect
of word-of-mouth recommendations by inviting regular users to indicate product ingredients in their
recommendations. Consequently, when companies or brands choose word-of-mouth recommendations
for marketing and promotion, in addition to choosing opinion leaders with high domain knowledge,
Volume 31 • Issue 2
17
they can also use a more cost-effective way to maximize the effect of word-of-mouth recommendations
by using ordinary users to label product ingredients in their recommendations.
There are some shortcomings in this research. First, in terms of research context, this research aimed
to explore the effect of recommendation terms on consumer product evaluation and purchase intention
in the context of online word-of-mouth recommendations. Future research could explore the different
effects of product recommendation terms in other contexts, for example, whether the effects of
recommendation terms also exist in the context of advertising, and product reviews. Such interesting
questions need to be addressed in future research. Second, in terms of the object of research, this
article only focused on the choice of recommendation terms for functional products. If future studies
could explore both functional and hedonic products and compare the differences between the two
types of products, the scope of application of the recommendation term effect could be broadened.
Third, in terms of the research question, future research could also consider the effect of culture
on recommendation terminology. Some regional cultures are more inclined toward the analytical
processing of information (Chu & Huang, 2017), and participants accustomed to processing information
holistically (as opposed to processing it separately) are more likely to view product ingredients and
product functions as a whole, with enough information diagnosable to form a perception of product
specialization. Consequently, whether the findings of this article apply to the cultural contexts of
these regions where analytical processing is dominant, remains to be explored in the future.
Fourth, in terms of research methodology, this research only conducted laboratory experiments
and not field experiments, which has some ecological validity limitations. Future studies could
conduct field experiments to enhance the ecological validity of the study. Fifth, in terms of technical
interventions, this research only focuses on the effects of interventions on the content of online word-
of-mouth recommendations on the recipients of the information. However, in addition to the content
of the recommendation, the recommendation system is also an important factor that interferes with
the effectiveness of online word-of-mouth recommendations (Resnick & Varian, 1997). Moreover,
in reality, with the rapid development of information technology, recommendation systems are
increasingly used on online shopping platforms and social media (Ramadan et al., 2019; Farah et
al., 2020). Future research could explore the impact of different types of automated recommender
systems on the effectiveness of online word-of-mouth recommendations.
Volume 31 • Issue 2
18
Arafeh, M., Ceravolo, P., Mourad, A., Damiani, E., & Bellini, E. (2021). Ontology based recommender system
using social network data. Future Generation Computer Systems, 115, 769–779. doi:10.1016/j.future.2020.09.030
PMID:33071400
Arndt, J. (1967). Role of product-related conversations in the diffusion of a new product. JMR, Journal of
Marketing Research, 4(3), 291–295. doi:10.1177/002224376700400308
Aviva, P. M., Costello, J. P., & Walker, R. R. (2022). Get your science out of here: When does invoking science in
the marketing of consumer products backfire? The Journal of Consumer Research, 49(5), 721–740. doi:10.1093/
jcr/ucac020
Baltas, G., Kokkinaki, F., & Loukopoulou, A. (2017). Does variety seeking vary between hedonic and utilitarian
products? The role of attribute type. Journal of Consumer Behaviour, 16(6), 1–12. doi:10.1002/cb.1649
Barreda, A. A., Bilgihan, A., Nusair, K., & Okumus, F. (2015). Generating brand awareness in Online Social
Networks. Computers in Human Behavior, 50, 600–609. doi:10.1016/j.chb.2015.03.023
Batra, R., & Ahtola, O. T. (1991). Measuring the Hedonic and Utilitarian Sources of Consumer Attitudes.
Marketing Letters, 2(2), 159–170. doi:10.1007/BF00436035
Botti, S., & Mcgill, A. L. (2011). The locus of choice: Personal causality and satisfaction with hedonic and
utilitarian decisions. The Journal of Consumer Research, 37(6), 1065–1078. doi:10.1086/656570
Brodie, R. J., Ilic, A., Juric, B., & Hollebeek, L. (2013). Consumer engagement in a virtual brand community:
An exploratory analysis. Journal of Business Research, 66(1), 105–114. doi:10.1016/j.jbusres.2011.07.029
Cheng, Y. M., Mukhopadhyay, A., & Schrift, R. Y. (2017). Do costly options lead to better outcomes? How the
protestant work ethic influences the cost-benefit heuristic in goal pursuit. JMR, Journal of Marketing Research,
54(4), 636–649. doi:10.1509/jmr.15.0105
Cheung, C., Bo, S. X., & Liu, I. (2014). Do actions speak louder than voices? The signaling role of social
information cues in influencing consumer purchase decisions. Decision Support Systems, 65(1), 50–58.
doi:10.1016/j.dss.2014.05.002
Chitturi, R., Raghunathan, R., & Mahajan, V. (2008). Delight by design: The role of hedonic versus utilitarian
benefits. Journal of Marketing, 72(3), 48–63. doi:10.1509/JMKG.72.3.048
Choi, S. M., & Rifon, N. J. (2002). Antecedents and consequences of web advertising credibility. Journal of
Interactive Advertising, 3(1), 12–24. doi:10.1080/15252019.2002.10722064
Chu, W. T., & Huang, W. H. (2017). Cultural difference and visual information on hotel rating prediction. World
Wide Web (Bussum), 20(4), 595–619. doi:10.1007/s11280-016-0404-2
Cohen, J. (Ed.). (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge. https://www.
science-open.com/document/vid/6ccbfdc0-1aae-487b-8672-4cd41c713105
Deval, H., Mantel, S. P., Kardes, F. R., & Posavac, S. S. (2013). How Naive Theories Drive Opposing Inferences
from the Same Information. The Journal of Consumer Research, 39(6), 1185–1201. doi:10.1086/668086
Dimoka, A., Hong, Y., & Pavlou, P. A. (2012). On product uncertainty in online markets: Theory and evidence.
Management Information Systems Quarterly, 36(2), 395–426. doi:10.2307/41703461
Fan, W. J., Liu, Y., Li, H. X., Tuunainen, V. K., & Lin, Y. Q. (2021). Quantifying the effects of online review?
Content structures on hotel review helpfulness. Internet Research, 32(7), 202–227. doi:10.1108/INTR-11-2019-
0452
Farah, M. F., Ramadan, Z. B., & Shatila, L. (2020). The examination of self-service replenishing solutions’
potential. International Journal of Web Based Communities, 16(2), 134. doi:10.1504/IJWBC.2020.107149
Grunert, K. G., & Wills, J. M. (2007). A review of European research on consumer response to nutrition
information on food labels. Zeitschrift für Gesundheitswissenschaften, 15(5), 385–399. doi:10.1007/s10389-
007-0101-9
Volume 31 • Issue 2
19
Haws, K. L., Reczek, R. W., & Sample, K. L. (2017). Healthy diets make empty wallets: The healthy = expensive
intuition. The Journal of Consumer Research, 43(6), 992–1007. doi:10.1093/jcr/ucw078
Hennig-Thurau, T., Walsh, G., & Walsh, G. (2003). Electronic word-of-mouth: Motives for and consequences
of reading customer articulations on the Internet. International Journal of Electronic Commerce, 8(2), 51–74.
doi:10.1080/10864415.2003.11044293
Higgins, E. T. (1996). Knowledge activation: accessibility, applicability, and salience. In E. T. Higgins & A.
W. Kruglanski (Eds.), Social psychology: Handbook of basic principles (pp. 133–168). The Guilford Press.
Hilligoss, B., & Rieh, S. Y. (2008). Developing a unifying framework of credibility assessment: Construct,
heuristics, and interaction in context. Information Processing & Management, 44(4), 1467–1484. doi:10.1016/j.
ipm.2007.10.001
Hovland, C., & Weiss, W. (1951). The influence of source credibility on communication effectiveness. Public
Opinion Quarterly, 15(4), 635–650. doi:10.1086/266350
Huang, X., Li, X., & Zhang, M. (2013). “Seeing” the social roles of brands: How physical positioning influences
brand evaluation. Journal of Consumer Psychology, 23(4), 509–514. doi:10.1016/j.jcps.2013.03.001
Hwang, Y., & Jeong, S. H. (2016). “This is a sponsored blog post, but all opinions are my own”: The effects
of sponsorship disclosure on responses to sponsored blog posts. Computers in Human Behavior, 62, 528–535.
doi:10.1016/j.chb.2016.04.026
Ki, C. W. C., & Kim, Y. K. (2019). The mechanism by which social media influencers persuade consumers:
The role of consumers’ desire to mimic. Psychology and Marketing, 36(10), 905–922. doi:10.1002/mar.21244
Kirmani, A., & Rao, A. R. (2000). No pain, no gain: A critical review of the literature on signaling unobservable
product quality. Journal of Marketing, 64(2), 66–79. doi:10.1509/jmkg.64.2.66.18000
Kramer, T., & Block, L. (2011). Nonconscious effects of peculiar beliefs on consumer psychology and choice.
Journal of Consumer Psychology, 21(1), 101–111. doi:10.1016/j.jcps.2010.09.009
Kyung, E. J., Thomas, M., & Krishna, A. (2017). When bigger is better (and when it is not): Implicit bias in
numeric judgments. The Journal of Consumer Research, 44(1), 62–79. doi:10.1093/jcr/ucw079
Lee, A. Y., Keller, P. A., & Sternthal, B. (2010). Value from regulatory construal fit: The persuasive impact of
fit between consumer goals and message concreteness. The Journal of Consumer Research, 36(5), 735–747.
doi:10.1086/605591
Lellis, J. C. (2016). Waving the red flag: FTC regulation of deceptive weight-loss advertising 1951–2009. Health
Communication, 31(1), 47–59. doi:10.1080/10410236.2014.936334 PMID:26075539
Ma, T., & Ding, F. (2018). Research on the dynamic effect of the intelligent urban experience to the tourists’
two-way Internet word-of-mouth. International Journal of Communication Systems, 31(16), 1–10. doi:10.1002/
dac.3467
Mai, R., Hoffmann, S., Lasarov, W., & Buhs, A. (2019). Ethical products = less strong: How explicit and
implicit reliance on the lay theory affects consumption behaviors. Journal of Business Ethics, 158(3), 659–677.
doi:10.1007/s10551-017-3669-1
Metzger, M. J., & Flanagin, A. J. (2013). Credibility and trust of information in online environments: The use
of cognitive heuristics. Journal of Pragmatics, 59(1/2), 210–220. doi:10.1016/j.pragma.2013.07.012
Molden, D. C., & Dweck, C. S. (2006). Finding/meaning in psychology: A lay theories approach to self-regulation,
social perception, and social development. The American Psychologist, 61(3), 192–203. doi:10.1037/0003-
066X.61.3.192 PMID:16594836
Moon, S. J., Costello, J. P., & Koo, D. M. (2017). The impact of consumer confusion from eco-labels on negative
WOM, distrust, and dissatisfaction. International Journal of Advertising, 36(2), 246–271. doi:10.1080/02650
487.2016.1158223
Moore, S. G. (2015). Attitude predictability and helpfulness in online reviews: The role of explained actions and
reactions. The Journal of Consumer Research, 42(1), 30–44. doi:10.1093/jcr/ucv003
Volume 31 • Issue 2
20
Nikolinakou, A., & Phua, J. (2020). Do human values matter for promoting brands on social media? How social
media users’ values influence valuable brand-related activities such as sharing, content creation, and reviews.
Journal of Consumer Behaviour, 19(1), 13–23. doi:10.1002/cb.1790
Park, D. H., & Lee, J. (2009). E-WOM overload and its effect on consumer behavioral intention depending
on consumer involvement. Electronic Commerce Research and Applications, 7(4), 386–398. doi:10.1016/j.
elerap.2007.11.004
Phua, J., & Tinkham, S. (2016). Authenticity in obesity public service announcements (PSAs): Influence of
spokesperson type, viewer weight and source credibility on diet, exercise, information seeking, and E-WOM
intentions. Journal of Health Communication, 21(3), 337–345. doi:10.1080/10810730.2015.1080326
PMID:26735263
Preacher, K. J., & Hayes, A. F. (2008). Contemporary approaches to assessing mediation in communication
research. In A. F. Hayes, M. D. Slater, & L. B. Snyder (Eds.), The Sage sourcebook of advanced data analysis
methods for communication research (pp. 13–54). Sage Publications. doi:10.4135/9781452272054.n2
Ramadan, Z. B., Farah, M. F., & Daouk, S. (2019). The effect of e-retailers’ innovations on shoppers’
impulsiveness and addiction in web-based communities: The case of Amazon’s Prime Now. International Journal
of Web Based Communities, 15(4), 327–343. doi:10.1504/IJWBC.2019.103181
Ran, K., & Zheng, Y. (2017). The effects of promotions on hedonic versus utilitarian purchases. Journal of
Consumer Psychology, 27(1), 59–68. doi:10.1016/j.jcps.2016.05.005
Ran, Y. X., Niu, Y. X., & Chen, S. Y. (2021). “More” is less: Why multiple payment mechanism impairs individual
donation. Acta Psychologica Sinica, 53(4), 413–430. doi:10.3724/SP.J.1041.2021.00413
Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM, 40(3), 56–58.
doi:10.1145/245108.245121
Rieh, S. Y., & Danielson, D. R. (2007). Credibility: A multidisciplinary framework. Annual Review of Information
Science & Technology, 41(1), 307–364. doi:10.1002/aris.2007.1440410114
Salerno, A., Laran, J., & Janiszewski, C. (2014). Hedonic eating goals and emotion: When sadness decreases
the desire to indulge. The Journal of Consumer Research, 41(1), 135–151. doi:10.1086/675299
Salloum, G., & Tekli, J. (2022). Automated and personalized meal plan generation and relevance scoring
using a multi-factor adaptation of the transportation problem. Soft Computing, 26(5), 2561–2585. doi:10.1007/
s00500-021-06400-1
Schlosser, A. E. (2011). Can including pros and cons increase the helpfulness and persuasiveness of online
reviews? The interactive effects of ratings and arguments. Journal of Consumer Psychology, 21(3), 226–239.
doi:10.1016/j.jcps.2011.04.002
Schwarz, N. (2004). Metacognitive experiences in consumer judgment and decision making. Journal of Consumer
Psychology, 14(4), 332–348. doi:10.1207/s15327663jcp1404_2
Smith, D., Menon, S., & Sivakumar, K. (2005). Online peer and editorial recommendations, trust, and choice
in virtual markets. Journal of Interactive Marketing, 19(3), 15–37. doi:10.1002/dir.20041
Stubb, C., Nyström, A. G., & Colliander, J. (2019). Influencer marketing: The impact of disclosing sponsorship
compensation justification on sponsored content effectiveness. Journal of Communication Management (London),
23(2), 109–122. doi:10.1108/JCOM-11-2018-0119
Thorson, K. S., & Rodgers, S. (2006). Relationships between blogs as eWoM and interactivity, perceived
interactivity, and parasocial interaction. Journal of Interactive Advertising, 6(2), 5–44. doi:10.1080/15252019
.2006.10722117
Verhagen, T., & Dolen, W. V. (2009). Online purchase intentions: A multi-channel store image perspective.
Information & Management, 46(2), 77–82. doi:10.1016/j.im.2008.12.001
Wakefield, L. T., & Bennett, G. (2018). Sports fan experience: Electronic word-of-mouth in ephemeral social
media. Sport Management Review, 21(2), 147–159. doi:10.1016/j.smr.2017.06.003
Volume 31 • Issue 2
21
Wan, E. W., Chen, R. P., & Jin, L. (2017). Judging a book by its cover? The effect of anthropomorphism on
product attribute processing and consumer preference. The Journal of Consumer Research, 43(6), 1008–1030.
doi:10.1093/jcr/ucw074
Wang, S. (2001). Cue-based trust in an online shopping environment: Conceptualization and propositions.
Marketing advances in pedagogy, process and philosophy, proceedings of the annual meeting of the society for
advances in marketing, 284-287.
Wang, S. W., & Scheinbaum, A. C. (2018). Enhancing brand credibility via celebrity endorsement: Trustworthiness
trumps attractiveness and expertise. Journal of Advertising Research, 58(1), 16–32. doi:10.2501/JAR-2017-042
Wolny, J., & Mueller, C. (2013). Analysis of fashion consumers’ motives to engage in electronic word-of-mouth
communication through social media platforms. Journal of Marketing Management, 29(5–6), 562–583. doi:1
0.1080/0267257X.2013.778324
Xu, J., & Wyer, R. S. Jr. (2010). Puffery in advertisements: The effects of media context, communication norms,
and consumer knowledge. The Journal of Consumer Research, 37(2), 329–343. doi:10.1086/651204
Yang, Y., Gu, Y., & Galak, J. (2016). When it could have been worse, it gets better: How favorable uncertainty
resolution slows hedonic adaptation. The Journal of Consumer Research, 43(5), 747–768. doi:10.1093/jcr/ucw052
Yim, Y. C., Yoo, S. C., Sauer, P. L., & Seo, J. H. (2014). Hedonic shopping motivation and co-shopper influence
on utilitarian grocery shopping in superstores. Journal of the Academy of Marketing Science, 42(5), 528–544.
doi:10.1007/s11747-013-0357-2
Yin, D., Bond, S. D., & Zhang, H. (2014). Anxious or Angry? Effects of Discrete Emotions on the Perceived
Helpfulness of Online Reviews. Management Information Systems Quarterly, 38(2), 539–560. doi:10.25300/
MISQ/2014/38.2.10
Zane, D. M., Smith, R. W., & Reczek, R. W. (2020). The meaning of distraction: How metacognitive inferences
from distraction during multitasking affect brand evaluations. The Journal of Consumer Research, 46(5),
974–994. doi:10.1093/jcr/ucz035
Yanshu Ji is a PhD student in the Department of Human Resource Management at Nanjing University. She
graduated with a Master’s degree from the University of Queensland Business School. Her research interests
include consumer psychology and decision making, organizational behavior and compensation management.
Wenjing Song is a PhD student in the Department of Marketing and E-Commerce at Nanjing University. She
graduated with a Master’s degree from the School of Management, Shanghai University. Her research papers have
been published or are being published in journals such as Nankai Business Review and Psychology & Marketing.
Her research interests are in consumer psychology and decision making and marketing communication.
Lei Yang is a PhD student in the Department of Human Resource Management at Nanjing University. She graduated
from the School of Economics and Management of Lanzhou Jiaotong University with a Master ’s degree. Her
research interests are in organizational behavior and technological innovation. Her research papers have been
published or are being published in Studies in Science of Science, Science Research Management, Science of
Science and Management of S.& T., Forum on Science and Technology in China, Journal of Guangxi University
of Finance and Economics and other journals.
Chunyan Jiang is a professor and PhD supervisor in the Department of Human Resource Management at Nanjing
University. Her research interests include organizational learning and emerging firm growth. Her research papers
have been officially published in Personal Review, Management and Organization Review, Frontiers of Business
Research in China, Journal of Management World, Journal of Management Sciences in China, Nankai Business
Review, Science of Science and Management of S.& T., Management Review, China Soft Science, Foreign
Economics & Management, Economic Science, Science Research Management, Human Resources Development
of China, Soft Science, and Research on Economics and Management. Her research has been supported by the
National Natural Science Foundation of China. She is currently the Director of the Emba Centre at Nanjing University.
Available via license: CC BY 3.0
Content may be subject to copyright.