=Paper=
{{Paper
|id=Vol-3691/paper4
|storemode=property
|title=Predictive Factors of Electronic Word of Mouth (eWOM) Intention Among University Students
|pdfUrl=https://ceur-ws.org/Vol-3691/paper4.pdf
|volume=Vol-3691
|authors=Olger Gutierrez-Aguilar,Diego Castillo-Carranza,Ronald Valdivia-Cornejo,Fiorela Ticona-Apaza,Valerio Ticona-Apaza
|dblpUrl=https://dblp.org/rec/conf/cisetc/Gutierrez-Aguilar23
}}
==Predictive Factors of Electronic Word of Mouth (eWOM) Intention Among University Students==
https://ceur-ws.org/Vol-3691/paper4.pdf
Predictive Factors of Electronic Word of Mouth (eWOM)
Intention Among University Students
Olger Gutierrez-Aguilar1, Diego Castillo-Carranza1, Ronald Valdivia-Cornejo1, Fiorela
Ticona-Apaza2 and Valerio Ticona-Apaza2
1
Universidad Católica de Santa María, Arequipa (Perú)
2
Universidad Nacional de San Agustín de Arequipa (Perú)
Abstract
The research objective was to establish significant relationships among the predictors of electronic
word-of-mouth intention, such as informative peers, internet information, and normative internet use
among university students. The methodology used for the study was non-experimental research; a
questionnaire was applied to a random sample of 127 students (n=13; α=0.870 ω=0.870), validity and
reliability tests, and exploratory factor analysis were used—covariance-based structural equation
modelling (CB-SEM). The results prove that the influence of Internet regulations is the most significant
on the intention to share electronic opinions (IEWOM), determining how people share opinions online.
Although the influence of acquaintances' opinions is also positive, it is not as strong as the normative
ones. However, the relationship between Internet information and the intention to share opinions was
insignificant in this study. The proposed model explains 35% of the variability in the intention to share
electronic opinions. Companies should consider the importance of regulations and acquaintances'
opinions when designing marketing strategies. Exploring more factors and contexts is essential to
understanding these influences better.
Keywords 1
Ewom, electronic word of mouth, normative internet, Informative Peer, internet information
1. Introduction
In today's digital age, various forces operating within the vast fabric of the virtual world shape
consumer decisions and perceptions. "Informational Peers" are pivotal figures, facilitating social
learning and offering insightful perspectives on various topics. This interaction is further
complemented and often amplified by Internet information, revolutionising how individuals
access and process data. However, all this flow of interaction and information accessibility is
framed within the Internet Regulations, which lay down behaviours and expectations in the
online realm. These elements coalesce in the intention behind "Electronic Word of Mouth"
(eWOM), reflecting people's willingness to share and act upon digital opinions and experiences.
This phenomenon has redefined "word of mouth" dynamics in the contemporary era,
underscoring peer interaction and online regulation as determinants in generating and trusting
shared opinions on digital platforms.
Numerous forces operate within the vast fabric of the digital world to influence consumer
decision-making. Informational Peers (IP) emerge as crucial figures facilitating social learning
and providing valuable perspectives on varied topics [1]. This peer interaction is intertwined
with Internet Information (II), which has transformed how people access and process
information [2]. However, this amalgamation of opinions and information is framed within the
CISETC 2023: International Congress on Education and Technology in Sciences 2023, December 04–06, 2023,
Zacatecas, Mexico
ogutierrez@ucsm.edu.pe (O. Gutierrez-Aguilar); 75085041@ucsm.edu.pe (D. Castillo-Carranza);
ronald.valdivia@ucsm.edu.pe (R. Valdivia-Cornejo); fticona@unsa.edu.pe (F. Ticona-Apaza); vticonaap@unsa.edu.pe
(V. Ticona-Apaza)
0000-0002-6657-7529 (O. Gutierrez-Aguilar); 0009-0008-6887-9329 (D. Castillo-Carranza); 0009-0002-8933-
1975 (R. Valdivia-Cornejo); 0000-0001-5745-2575 (F. Ticona-Apaza); 0000-0001-6884-1680 (V. Ticona-Apaza)
© 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
CEUR
ht
tp:
//
ceur
-ws
.or
g
Works
hop I
SSN1613-
0073
Pr
oceedi
ngs
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Internet Regulations (NI), which establish a set of behaviours and expectations in the online
environment [3]. These elements converge in eWOM Intent (IEW), reflecting people's willingness
to share and act based on digital opinions and experiences, which has redefined the dynamics of
"word of mouth" in the modern era [4].
"Information Peers" (IPs) are essential in information transmission and absorption, acting as
crucial nodes in networks and communities. This peer influence is a form of "social learning," a
process through which people interpret and make sense of reality based on the information they
obtain from others [1]. Furthermore, in uncertain situations, such as when facing unknown
markets, these informational pairs become guides, helping others navigate and make informed
decisions [5].
The quality of the information that these peers offer is of particular importance. Not only do
they provide insights into past actions or performances, but they can also offer constructive
suggestions on how to approach and improve in future situations, providing dual value in their
feedback [6]. However, it is essential to recognise that the impact of informational pairs may vary
depending on the context. In some scenarios, such as digital, other information sources, such as
the Internet, can overcome the influence of peers in terms of mediation [7]. Despite this, in areas
such as public interest, the presence and alignment of informational peers remain vital, as
regulators are expected to network with these informational peers [8].
Although the literature has established the relevance of peer informational influence, there
are nuances in its perception and effect. Authors such as Barber [9], Mangleburg [10], and X. Wang
[11] have highlighted its importance. However, it has also been observed that its impact can vary
demographically, being more pronounced in certain groups, such as young people [12].
"Informational Peers," conceptualised as entities whose private signals are intrinsically
connected and correlated, are instrumental in promoting learning and informed decision-making
within their communities [13].
The rise of the digital age has intensified the accessibility and availability of information
globally. Central to this phenomenon is "Internet Information" (II), which reflects how individuals
process, interpret, and act on online information.
The Internet, from its normative and informational dimensions, plays a medium role in the
way in which it is used and interacted on the platform, especially in the context of the propagation
of opinions and comments, known as "electronic word of mouth" or eWOM [14]. The ability of the
Internet to reduce geographical barriers and promote global communication is unquestionable.
However, it is vital to discern the relevance and usefulness of the information circulating on the
network [15].
The individual's ability to navigate and verify information online is an essential competency
in the digital age and directly impacts specific behaviours, such as interaction with e-commerce.
These skills are intrinsically linked to individuals' propensity to use the Internet for information
[16]. In addition, demographic variables, such as age, play a determining role in the perception
and relationship with online information, exemplifying the complexity of the relationship
between users and the platform [7].
The Internet is not simply a repository of information; it reflects and captures consumer
trends and global perceptions, serving as a barometer of current market dynamics [17]. This
transformative impact is further highlighted when considering the rapid adoption and adaptation
of the platform, radically altering traditional forms of information consumption [2]. The
prevalence of the Internet in the commercial field is solidifying, displacing traditional channels
and redefining market strategies [18].
The versatility of the Internet is also manifested in its ability to predict phenomena from large
data sets, which underlines its relevance in multidisciplinary research fields [19]. The accelerated
pace of information dissemination through the Internet surpasses many conventional methods,
evidencing its effectiveness and reach [20]. Finally, it is essential to consider the psychosocial
dimensions of the Internet. The platform influences fundamental areas of human life, from
interpersonal relationships to psychological well-being, and this influence can be intrinsically
linked to personal characteristics and personality traits [21, 22].
The emergence of the Internet into daily life has led to specific regulations and behaviours
associated with using the platform. The "Internet Regulation" variable is central to this
phenomenon, which reflects how Internet rules and regulations affect and influence user
behaviour. With eWOM, it has been noted that both normative and informational perspectives of
the Internet mediate the relationship between platform use and the propagation of opinions [14].
Beyond interaction on social networks, high school students who follow normative Internet use
show significantly higher academic performance [23]. However, it has been detected that the
combination of normative behaviour with problematic Internet use can be linked to different
levels of metacognitive strategies among students [23].
Student adjustment in university environments and its relationship to normative Internet use
remains an area that requires greater clarity [3]. On the other hand, age was found to have an
inverse relationship with the normative influence of the Internet, suggesting that different age
groups could have different normative approaches towards the platform [7].
Internet regulations also influence online social interactions, with factors such as homophily
and informational influence playing a role in eWOM behaviours such as seeking, transmitting, and
creating information [24]. In electronic commerce, regulations can influence impulsive
purchasing behaviours, evidencing how regulations affect online decision-making [25].
Furthermore, data-sharing regulations positively correlate with users searching for information
on the Internet, underscoring the importance of regulations in information-seeking practices
[26].
Perceptions and behaviours in e-commerce are also affected by the reliability of the Internet
and its associated regulations. These norms have been observed to impact attitudes toward
online shopping and purchasing decisions [27]. Likewise, it was highlighted that subjective norms
and perceived behavioural control influence attitudes towards ethical behaviours on the Internet
[28].
In today's digital world, electronic word of mouth, known as eWOM, has established itself as a
powerful force influencing consumer decisions and perceptions. eWOM Intent refers to the
willingness of individuals to engage, share, and act on other users' online opinions and comments.
The act of sharing eWOM can strengthen the trust of the sharing individual, which can
consequently increase their purchase intention [29]. However, it is crucial to note that this trust
effect may be limited by the strength of the social tie with the source of eWOM [29]. In e-
commerce, eWOM has played a crucial role in improving interactions and exchange of opinions
between potential and existing buyers, improving conversations and interactions [30].
eWOM sharing behaviour varies and can range from distributing product-related information
initially posted by others [4] to generating entirely original opinions. In this sense, eWOM has
evolved from traditional word-of-mouth methods, allowing consumers to convey their opinions
through various digital platforms. This phenomenon allows academics and professionals to
accurately measure people's attitudes toward a company or product [31]. While some studies
indicate that eWOM has no significant relationship with consumer loyalty, others suggest a
positive association between eWOM and customer satisfaction, especially in experiential
products [32]. With the popularity of social media, eWOM has primarily replaced traditional word
of mouth, introducing a new dynamic in how consumers interact and influence each other [33].
From a broader perspective, eWOM acts as a flow of information that can affect the well-being
outcomes of all participants involved [34]. As social media continues to grow and mature, online
customers have more opportunities than ever to generate eWOM, which can significantly
influence the decision-making process of other individuals [35]. Finally, it is essential to highlight
that attitudes towards eWOM, especially negative ones, can affect consumer behaviour, such as
increased panic buying activity and changes in brand preferences [36, 37]. Furthermore, it has
been observed that age can indirectly affect eWOM intentions, primarily mediated by the
influence of online information [7].
The exchange of information and decisions based on it have become intricately complex.
Informational Pairs (IP), a concept based on social learning and the search for meaning about
reality [1], have acquired unprecedented relevance. These peers act as essential intermediaries,
providing perspectives based on personal experiences that sometimes guide others in uncertain
markets [5]. However, this information exchange is amplified and complemented by Internet
Information (II), a tool that has radically transformed how information is accessed and used [2].
The confluences between the opinions of peers and the information accessible on the web create
an environment of rich and diversified information.
While the interaction between IP and II shapes perception, the Internet Regulations (NI) act
as a kind of compass, offering a framework for behaviour and expectations in the digital world
[3]. This regulation can influence how opinions and experiences shared online are perceived and
how much trust is placed in them. In fact, the trust generated by sharing eWOM, or "virtual word
of mouth," can be significantly influenced by this interaction between peers and online
information [29]. This amalgamation of influences culminates in what we know as eWOM
Intention (IEW). This phenomenon reflects people's willingness to share and act on opinions and
experiences in the digital sphere [4]. However, it is essential to recognize that Internet regulations
also play a determining role in forming and disseminating eWOM. Online norms and expectations,
such as trust in product reviews on specific platforms, can influence perceptions of authenticity
and, thus, eWOM behaviour [27]. Based on the previous considerations, the following hypotheses
are being suggested:
• H1: Informational Peers (IP) statistically affect eWOM Intention (IEW).
• H2: Internet Information (II) statistically impacts eWOM Intention (IEW).
• H3: Internet Regulation (NI) has a statistically significant effect on eWOM Intention
(IEW).
2. Methodology
This study was carried out with a sample of 127 university students, with 47 men representing
37.0% and 80 women representing 63.08%. The ages range from 16 to 30 years old, with a mean
of 21.31 and an SD of 2.57. The instrument was applied between July and August 2023. The
methodology applied for the study is a non-experimental investigation, and the sample extraction
was random through prior acceptance to collaborate with the study. After investigating related
research, the instrument has been created for the present study to determine the most
appropriate variables. The initial instrument had five variables: Informative Peer (IP= 3 items),
Internet information (II= 3 items), Normative internet (NI= 3 items), Normative Peer (NP) and
Electronic word of mouth eWOM (IEW= 5 items). The Normative Peer variable (NP = 3 items)
was discarded due to the poor results in the exploratory factor analysis (EFA) and the
confirmatory factor analysis (CFA). This variable did not support the required quality adjustment
indices or the reliability and consistency in the proposed model. The instrument structure is on a
5-point Likert scale for measurement: (1) strongly disagree; (2) partly disagree; (3) neither agree
nor disagree; (4) partly agree; (5) strongly agree. For the statistical analysis, CB-SEM or
Covariance-Based Structural Equation Modeling was used, which made it possible to work with
latent or not directly observed variables. Jamovi v: 2.3.24.0 software was used to process the data.
3. Results
In the reliability study, Cronbach's Alpha coefficient was used, obtaining a value of α=0.870, and
the McDonald Coefficient, with a result of ω=0.870. These indicators suggest a satisfactory level
of reliability. In order to evaluate the adequacy of the items concerning their factors, the Kaiser-
Meyer-Olkin (KMO) test was applied, yielding a value of 0.800, which suggests an acceptable
adequacy of the items analyzed. The Bartlett Sphericity Test also presented the following results:
χ2=585.15, degrees of freedom (df) = 78, and p≤0.001, which indicates relevant significance.
The variables presented in Table 1 provide a comprehensive view of the reliability and
convergent validity of certain constructs in a study. Each variable has been assessed using
multiple items, a shared research practice to ensure that constructs are measured accurately and
validly. The IP variable shows robust internal consistency, with Cronbach's Alpha 0.76 and Omega
values around 0.78. This suggests that the items associated with this construct are highly
correlated, providing a coherent measure. Furthermore, an AVE of 0.55 indicates that more than
half of the variance of the items can be attributed to the construct, a positive sign of convergent
validity.
On the other hand, variable II presents similar values in terms of reliability, although slightly
lower than IP. However, its AVE of 0.50 suggests that it is correct at the acceptable limit for
convergent validity, which could lead to considering a revision of its items or a more cautious
interpretation of its results. The NI variable offers solid reliability, similar to IP. Its AVE, although
acceptable, is slightly above the limit, suggesting that the variance explained by the construct is
sufficient but could benefit from additional review.
Finally, IEW stands out for its high reliability. A Cronbach's Alpha of 0.84 and consistent Omega
values suggest that the items for this construct have been carefully selected and provide a
cohesive measurement. Furthermore, an AVE of 0.57 supports its convergent validity. Together,
these metrics reinforce the quality and precision of the measures used in the study. However, as
in all research, it is essential to consider these results in the context of the study design, the
population of interest, and the limitations inherent to any measurement instrument. It is essential
that, in future studies, these items continue to be monitored and adjusted to ensure their
relevance and precision in the evaluation of the constructs of interest.
Table 1
Reliability indices
Variable α ω₃ AVE
Informative Peer (IP) 0.76 0.78 0.55
Internet information (II) 0.72 0.72 0.50
Normative internet (NI) 0.75 0.75 0.51
Electronic word of mouth - eWOM (IEW) 0.84 0.84 0.57
Table 2 shows the rotated component matrix obtained using the principal component analysis
extraction method and the oblimin rotation method. Below, we present the results:
Factor 1 - Informational Peers (IP): IP1, IP2, and IP3 load highly on this factor, suggesting that
they are strongly related to how individuals perceive, process, or value information from their
peers or personal contacts. This factor could be capturing the trust or importance that people give
to the information received from their acquaintances.
Factor 2 - Internet Information (II): Items II1, II2 and II3 have strong factor loadings here,
which could indicate how individuals interact with online information, their trust in online
sources or how they value the information obtained from websites and other online resources.
Factor 3 - Internet Regulations (NI): NI1, NI2, and NI3 are grouped in this factor. This could
relate to individuals' perceptions or attitudes towards Internet use rules, regulations or norms.
For example, it could address privacy, security, and the ethics of using information online.
Factor 4, EWOM (Electronic Word of Mouth) Intention, is linked to items IEW2, IEW3, IEW4,
and IEW5. Item IEW1 was not included due to adjustment issues. This factor pertains to sharing
consumer opinions online, such as product reviews and recommendations. This factor could be
capturing people's propensity to participate in EWOM, either by sharing their own opinions or
valuing the opinions of others.
The uniqueness of each item still indicates how much of its variability is not explained by the
factors. For example, IP3 has a uniqueness of 0.28, suggesting that there are aspects of how
individuals perceive "Informative Pairs" that are not fully captured by these four factors.
Table 2
Rotated Component Array
Factor
1 2 3 4 Unicidad
IP1 0.63 0.60
IP2 0.69 0.44
IP3 0.82 0.28
II1 0.66 0.50
II2 0.58 0.53
II3 0.73 0.42
NI1 0.58 0.58
NI2 0.74 0.35
NI3 0.75 0.44
IEW2 0.56 0.57
IEW3 0.76 0.37
IEW4 0.75 0.38
IEW5 0.84 0.29
Note. The ‘Minimum Residue’ extraction method was used in combination with an ‘oblimin’ rotation.
Table 3 reveals notable robustness in the Electronic word of mouth (eWOM) measure with a
Cronbach's Alpha of 0.839 and an AVE of 0.677, reflecting the coherence and robustness with
which people interact with online opinions. This construct stands out particularly in comparison
to the others, suggesting a significant influence of electronic word of mouth in today's society. On
the other hand, Internet Information and Informative Peers present consistent values. However,
they are lower than eWOM, possibly due to the diversity of sources and types of information
online and the variability in how people value recommendations from their peers. Specifically,
Internet Information has an AVE of 0.631, and Informative Peer has an AVE of 0.656. With an AVE
of 0.664, Internet Regulations indicate a consistent understanding and assessment of online
regulations, although there may be variability in how they are interpreted individually. These
results underline the growing importance of eWOM and the complexity of people's interaction
with information and regulations in the digital environment.
Table 3
Reliability and construct validity
Cronbach's rho_A Composite (AVE)
Alpha reliability
Electronic word of mouth - eWOM (IEW) 0.839 0.848 0.893 0.677
Internet information (II) 0.722 0.786 0.835 0.631
Informative Peer (IP) 0.758 0.859 0.847 0.656
Normative internet (NI) 0.748 0.786 0.855 0.664
Table 4 examines the discriminant validity of the constructs using the Fornell-Larcker criterion
[38], This metric is essential to ensure that each construct or latent variable is distinctive and
does not significantly overlap with other constructs. According to Fornell and Larcker,
discriminant validity is present if the square root of a construct's variance extracted (AVE) is
greater than the correlations between that construct and any other. In other words, each
construct should share more variance with its items than other constructs [39]. Validity tests are
performed to determine how much a given construct differs from other constructs [40]. In the
analysis, Electronic word of mouth - eWOM (IEW) has a value of 0.823 on its diagonal, meaning
that its AVE's square root is 0.823. Comparing this value with the correlations in its row (0.266
with II, 0.341 with IP and 0.451 with NI), it is evident that 0.823 is more significant than any of
these correlations.
Likewise, Internet Information (II) has a diagonal value of 0.794, which is also higher than the
correlations in its row (0.266 with IEW, 0.156 with IP, and 0.317 with NI). Informative Peer (IP)
presents a diagonal value of 0.810, exceeding its correlations with other constructs (0.341 with
IEW, 0.156 with II and 0.283 with NI). Normative Internet (NI) has a value of 0.815 on its diagonal,
which is higher than the correlations in its row (0.451 with IEW, 0.317 with II, and 0.283 with IP).
Following the Fornell-Larcker criterion, it can be concluded that each construct has discriminant
validity. Each construct is essentially independent and different from the others, which reinforces
the solidity of the model and the clarity of the dimensions that are being measured.
Table 4
Fornell-Larcker criterion
IEW II IP NI
Electronic word of mouth - eWOM (IEW) 0.823
Internet information (II) 0.266 0.794
Informative Peer (IP) 0.341 0.156 0.810
Normative internet (NI) 0.451 0.317 0.283 0.815
In Table 5, the Heterotrait-Monotrait (HTMT) criterion was used, a relatively recent research tool
to evaluate discriminant validity between constructs. When using the HTMT to establish
discriminant validity, specific threshold values should not be exceeded., commonly 0.85 or, in
some cases, even 0.9. If the HTMT value between two constructs is less than this threshold, this
indicates a sufficient difference between the constructs, and therefore, they possess discriminant
validity.
In other words, if the HTMT ratio is less than 0.85 (or 0.9, depending on the criterion adopted),
it suggests that the constructs are distinctive and there is no significant overlap between them. In
the context you provided, all HTMT values were significantly below both thresholds, reaffirming
the discriminant validity of the constructs in the study.
Table 5
Heterotrait Criterion - Monotrait –HTMT
IEW II IP NI
Electronic word of mouth - eWOM (IEW)
Internet information (II) 0.316
Informative Peer (IP) 0.373 0.274
Normative internet (NI) 0.559 0.449 0.300
Figure 1: Model of CB-SEM
Figure 1 presents a model that examines how three predictor variables (Informative Peers – IP,
Internet Information – II, and Internet Normative – NI) influence the dependent variable EWOM
Intention (IEW). The R squared (R²) for IEW: 0.35. This indicates that approximately 35% of the
variability in EWOM Intention (IEW) can be explained by the three predictor variables in the
model. This is a considerable proportion and suggests that these variables significantly impact
EWOM intention. However, 65% of the variability is still influenced by other factors not included
in this model. The relationship between IP (Informative Peers) and IEW: Beta coefficient (β): 0.22.
This implies that a unit increase in IP, holding other variables constant, is associated with a 0.22
unit increase in EWOM intention. The z statistic: 1.95. This value is close to the typical threshold
of 1.96 for 5% significance. This suggests that the relationship between IP and IEW is marginally
significant.
The relationship between II (Internet Information) and IEW: Beta coefficient (β): 0.11. A unit
increase in II is related to a 0.11 increase in EWOM intention, holding other variables constant. Z-
statistic: 0.93. The relationship is not statistically significant since the z value is well below the
typical threshold.
The relationship between NI (Internet Regulation) and IEW: Beta coefficient (β): 0.42. This
suggests that NI has the most substantial impact on IEW among the three predictors. A unit
increase in NI is associated with a 0.42 unit increase in EWOM intention. Z-statistic: 3.03. With a
value higher than the threshold of 1.96, the relationship between NI and IEW is highly significant.
The model proposes that Internet regulations (NI) have the most pronounced impact on
EWOM intention, with this relationship being statistically significant. Informational Peers (IP)
have a moderate and marginally significant effect. On the other hand, Internet Information (II)
appears to have a minor impact and is not statistically significant in predicting EWOM intention
in this model.
Table 6
Path coefficients - Mean, STDEV, T values, p values
Sample Standard
Hypothesis Original mean deviation T statistics
sample (O) (M) (STDEV) (|O/STDEV|) P values Results
H1: II -> IEW 0.121 0.139 0.090 1.347 0.178 Rejected
H2: IP -> IEW 0.223 0.235 0.086 2.592 0.010 Supported
H3: NI -> IEW 0.349 0.347 0.097 3.617 0.000 Supported
Table 6 presents the path coefficients for three hypotheses, showing the relationship between
different constructs (II, IP, NI) and the IEW construct. Path coefficients, or betas, represent the
magnitude and direction of the relationship between variables. Additionally, statistical values
(mean, standard deviation, t-statistics, and p-values) are provided to evaluate the significance
and robustness of these relationships. Hypothesis H1 examines the relationship between Internet
Information (II) and Electronic word of mouth (eWOM) (IEW). The positive coefficient of 0.121
indicates a positive relationship but is not very strong. The p-value of 0.178 is more significant
than the standard threshold of 0.05, meaning that this relationship is not statistically significant
at that level. Therefore, there is not enough evidence to claim that Internet Information (II)
significantly impacts eWOM (IEW).
Hypothesis H2 examines the relationship between Informational Peers (IP) and eWOM (IEW).
The positive coefficient of 0.223 suggests a moderate positive relationship. The p-value of 0.010
is less than 0.05, indicating that this relationship is statistically significant. Therefore,
Informational Peers (IP) significantly impact eWOM (IEW).
Hypothesis H3 evaluates the relationship between Internet Regulation (NI) and eWOM (IEW).
The coefficient of 0.349 suggests a stronger positive relationship than the other two hypotheses.
The p-value is 0.000, clearly less than 0.05, indicating statistical significance. This implies that the
Internet Regulation (NI) significantly impacts eWOM (IEW). Thus, while Internet Information (II)
does not seem to have a significant impact on eWOM (IEW), both Informational Peers (IP) and
Internet Regulation (NI) have significant relationships with eWOM, with the relationship of
Internet Regulations the strongest of the three.
4. Conclusions and discussion
Regarding the significant influence of Internet regulations, of the three predictor variables
analyzed, the Internet Regulations (NI) proved to have the most significant influence on the
Intention of Electronic Word of Mouth (IEW). A unit increase in perceptions or attitudes toward
Internet regulations was related to a significant increase in intention to participate in EWOM. This
could suggest that online regulations and standards are crucial in determining how people share
and trust opinions online.
The influence of Informational Peers (IP) on EWOM intention was positive and marginally
significant. Although its impact was not as strong as Internet regulations, it is still relevant. This
highlights the importance of peer-to-peer word of mouth and how recommendations and
opinions from well-known and trusted people can influence the intention to share or trust online
opinions.
The relationship between Internet Information (II) and EWOM intention was not found to be
statistically significant in this study. Although this does not negate the importance of online
information, it indicates that, in the context of this study, other factors (such as regulations and
peer opinions) may be more determined in the intention to participate in EWOM.
The proposed model was able to explain approximately 35% of the variability in EWOM
Intention. This indicates a significant contribution of the predictor variables to EWOM behaviour,
although a considerable percentage of variability is explained by other factors not included in this
study.
Online companies and platforms should consider the importance of regulations and peer
influence when designing marketing strategies or implementing platform features; encouraging
positive word of mouth and ensuring clear and favourable regulations can boost users' intention
to participate in EWOM.
Exploring other factors that could influence EWOM intention would be beneficial to gain a
more complete understanding. Additionally, studies could be conducted in different contexts or
demographics to see if these results are consistent across diverse settings. This study provides
valuable insights into the factors influencing the intention to participate in the Electronic Word
of Mouth, underscoring the preeminence of online regulations and the role of peer
recommendations.
The results show a strong correlation between Informational Peers (IP) and eWOM Intention
(IEW), suggesting that IPs play a fundamental role in forming opinions and digital experiences.
However, a weak correlation or no correlation could indicate that although PIs are relevant, other
forces could influence IEW. This idea of PIs facilitating social learning [1]. Furthermore, the
guiding role of IPs, especially in uncertain markets [5].
If the results demonstrate that Internet Information (II) significantly impacts IEW, it would
highlight the dominance of online information in consumer decision formation. The
transformation in how people access and process information due to II [2]. Furthermore, the
possibility of digital sources outweighs the influence of IPs, especially considering demographic
factors [7].
Internet rules and regulations can be decisive in guiding consumer decisions if our results
show a notable effect of Internet Regulations (NI) on IEW. This critical role of NI in shaping online
behaviours and expectations [3]. Furthermore, the relevance of normative behaviours in various
contexts, from eWOM to academic performance [14] and [23].
If a strong tendency among individuals to engage in eWOM is observed in our results, it would
indicate a shift from traditional forms of word of mouth toward digital platforms. This
transformation is like word of mouth in the digital age [4]. Implications related to trust and how
the strength of social ties in the context of eWOM can limit it [29].
References
[1] S.-J. Park, Y.-R. Lee, and S. Borle, "The shape of Word-of-Mouth response function,"
Technological Forecasting and Social Change, vol. 127, pp. 304-309, 2018/02/01/ 2018, doi:
https://doi.org/10.1016/j.techfore.2017.10.006.
[2] E.-I. Varga, "How does the Internet Influences the Readers’ Behavior," Procedia
Manufacturing, vol. 46, pp. 949-956, 2020/01/01/ 2020, doi:
https://doi.org/10.1016/j.promfg.2020.05.013.
[3] R. P. Lanthier and R. C. Windham, "Internet use and college adjustment: the moderating role
of gender," Computers in Human Behavior, vol. 20, no. 5, pp. 591-606, 2004/09/01/ 2004,
doi: https://doi.org/10.1016/j.chb.2003.11.003.
[4] S. Gong, J. Zhang, P. Zhao, and X. Jiang, "Tweeting as a marketing tool: A field experiment in
the TV industry," Journal of Marketing Research, vol. 54, no. 6, pp. 833-850, 2017, doi:
https://doi.org/10.1509/jmr.14.0348.
[5] M. Blomkvist, T. Korkeamäki, and T. Takalo, "Learning and staged equity financing," Journal
of Corporate Finance, vol. 74, p. 102217, 2022/06/01/ 2022, doi:
https://doi.org/10.1016/j.jcorpfin.2022.102217.
[6] M. Gielen and B. De Wever, "Structuring peer assessment: Comparing the impact of the
degree of structure on peer feedback content," Computers in Human Behavior, vol. 52, pp.
315-325, 2015/11/01/ 2015, doi: https://doi.org/10.1016/j.chb.2015.06.019.
[7] A. Mishra, S. S. Maheswarappa, M. Maity, and S. Samu, "Adolescent's eWOM intentions: An
investigation into the roles of peers, the Internet and gender," Journal of Business Research,
vol. 86, pp. 394-405, 2018/05/01/ 2018, doi:
https://doi.org/10.1016/j.jbusres.2017.04.005.
[8] A. Schrank, "Regulators without borders? Latin American labour inspectors in transnational
context," Global Networks, Article vol. 21, no. 4, pp. 723-748, 2021, doi:
https://doi.org/10.1111/glob.12306.
[9] N. A. Barber, "Investigating the potential influence of the internet as a new socialization
agent in context with other traditional socialization agents," Journal of Marketing Theory and
Practice, vol. 21, no. 2, pp. 179-194, 2013, doi: https://doi.org/10.2753/MTP1069-
6679210204.
[10] T. F. Mangleburg, P. M. Doney, and T. Bristol, "Shopping with friends and teens’ susceptibility
to peer influence," Journal of retailing, vol. 80, no. 2, pp. 101-116, 2004, doi:
https://doi.org/10.1016/j.jretai.2004.04.005.
[11] X. Wang, C. Yu, and Y. Wei, "Social media peer communication and impacts on purchase
intentions: A consumer socialization framework," Journal of interactive marketing, vol. 26,
no. 4, pp. 198-208, 2012, doi: https://doi.org/10.1016/j.intmar.2011.11.004.
[12] G. B. Nkansah and Z. Papp, "Does cohort size matter? Assessing the effect of youth cohort
size and peer influence on young people's electoral participation," Journal of Youth Studies,
Article vol. 26, no. 7, pp. 859-877, 2023, doi: 10.1080/13676261.2022.2053666.
[13] Y. Kong, "Dominantly Truthful Multi-task Peer Prediction with a Constant Number of Tasks,"
Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms (SODA), 0, pp. 2398-
2411, 2019, doi: https://doi.org/10.1137/1.9781611975994.147.
[14] M. Bilal, Z. Jianqiu, U. Akram, Y. Tanveer, T. Sardar, and H. Rasool, "Understanding the effects
of internet usage behavior on ewom," International Journal of Information Systems in the
Service Sector, Review vol. 12, no. 3, pp. 93-113, 2020, doi:
https://doi.org/10.4018/IJISSS.2020070106.
[15] S. Mulla and A. Palave, "Moderation technique for sexually explicit content," in International
Conference on Automatic Control and Dynamic Optimization Techniques, ICACDOT 2016,
2017, pp. 56-60, doi: http://doi.org/10.1109/ICACDOT.2016.7877551.
[16] S. De Marco, J. M. Robles, M. Antino, and E. G. Fernandez, "La influencia de las habilidades
digitales para la búsqueda de información comercial on-line sobre el e-shopping: cuando las
desigualdades digitales afectan a los negocios," Journal Globalization, Competitiveness and
Governability, Article vol. 10, no. 3, pp. 136-152, 2016, doi:
http://doi.org/10.3232/GCG.2016.V10.N3.05.
[17] A. Al Hadwer, M. Tavana, D. Gillis, and D. Rezania, "A Systematic Review of Organizational
Factors Impacting Cloud-based Technology Adoption Using Technology-Organization-
Environment Framework," Internet of Things, vol. 15, p. 100407, 2021/09/01/ 2021, doi:
https://doi.org/10.1016/j.iot.2021.100407.
[18] N. Angulo, "digital marketing trends to watch in 2016 and beyond," Marketing Dive, 2016.
[19] W. Yuan, H. Du, J. Li, and L. Li, "A Multi-Scale Method for PM2.5 Forecasting with Multi-
Source Big Data," Journal of Systems Science and Complexity, Article vol. 36, no. 2, pp. 771-
797, 2023, doi: https://doi.org/10.1007/s11424-023-1378-7.
[20] H. Sharma and A. G. Aggarwal, "Finding determinants of e-commerce success: a PLS-SEM
approach," Journal of Advances in Management Research, vol. 16, no. 4, pp. 453-471, 2019,
doi: https://doi.org/10.1108/JAMR-08-2018-0074.
[21] J. M. Cénat, "Globalization, internet and psychiatric disorders: Call for research and action in
global mental health," Neurology, Psychiatry and Brain Research, vol. 36, pp. 27-29,
2020/06/01/ 2020, doi: https://doi.org/10.1016/j.npbr.2020.02.007.
[22] C. M. Kokkinos and N. Antoniadou, "Cyber-bullying and cyber-victimization among
undergraduate student teachers through the lens of the General Aggression Model,"
Computers in Human Behavior, vol. 98, pp. 59-68, 2019/09/01/ 2019, doi:
https://doi.org/10.1016/j.chb.2019.04.007.
[23] I. Bogdanovskaya, N. Koroleva, O. Khodakovskaia, A. Provorova, A. Uglova, and A. Alekhin,
"Metacognitive strategy of students with problematic internet use," in CEUR Workshop
Proceedings, 2021, vol. 2813, pp. 350-361, doi: https://doi.org/10.32744/pse.2021.6.18.
[24] C. Á. Santamaría, C. R. Gil, and A. V. R. Vega, "Conductucas eWOM:¿ qué factores contribuyen
en la creación, búsqueda y/o transmisión de información en RR. SS.?," 2020 2020: Instituto
Politécnico de Bragança, p. 237.
[25] J. Cavazos-Arroyo and A. I. Máynez-Guaderrama, "Antecedents of Online Impulse Buying: An
Analysis of Gender and Centennials’ and Millennials’ Perspectives," Journal
of Theoretical and Applied Electronic Commerce Research, vol. 17, no. 1, pp. 122-137doi:
http://doi.org/10.3390/jtaer17010007.
[26] Y. Kim and S. Nah, "Internet researchers’ data sharing behaviors: An integration of data
reuse experience, attitudinal beliefs, social norms, and resource factors," Online information
review, vol. 42, no. 1, pp. 124-142, 2018.
[27] N. Hayat, N. R. Zainol, T. Abir, A. Al Mamun, A. A. Salameh, and M. Mahshar, "Online Insurance
Purchase Intention and Behaviour among Chinese Working Adults," in Impact of Artificial
Intelligence, and the Fourth Industrial Revolution on Business Success, Cham, B. Alareeni and
A. Hamdan, Eds., 2023// 2023: Springer International Publishing, pp. 341-362.
[28] Y.-Y. Wang, Y.-S. Wang, and Y.-M. Wang, "What drives students’ Internet ethical behaviour:
an integrated model of the theory of planned behaviour, personality, and Internet ethics
education," Behaviour & Information Technology, vol. 41, no. 3, pp. 588-610, 2022/02/17
2022, doi: http://doi.org/10.1080/0144929X.2020.1829053.
[29] Y. Gvili and S. Levy, "I Share, Therefore I Trust: A moderated mediation model of the
influence of eWOM engagement on social commerce," Journal of Business Research, vol. 166,
p. 114131, 2023/11/01/ 2023, doi: https://doi.org/10.1016/j.jbusres.2023.114131.
[30] N. Hajli, J. Sims, A. H. Zadeh, and M.-O. Richard, "A social commerce investigation of the role
of trust in a social networking site on purchase intentions," Journal of business research, vol.
71, pp. 133-141, 2017.
[31] C.-L. A. Yen and C.-H. H. Tang, "The effects of hotel attribute performance on electronic word-
of-mouth (eWOM) behaviors," International Journal of Hospitality Management, vol. 76, pp.
9-18, 2019.
[32] M. Srivastava and S. Sivaramakrishnan, "The impact of eWOM on consumer brand
engagement," Marketing Intelligence & Planning, vol. 39, no. 3, pp. 469-484, 2021, doi:
https://doi.org/10.1108/MIP-06-2020-0263.
[33] Indrawati, P. C. Putri Yones, and S. Muthaiyah, "eWOM via the TikTok application and its
influence on the purchase intention of somethinc products," Asia Pacific Management
Review, vol. 28, no. 2, pp. 174-184, 2023/06/01/ 2023, doi:
https://doi.org/10.1016/j.apmrv.2022.07.007.
[34] A. Zaib Abbasi, N. Alqahtani, R. H. Tsiotsou, U. Rehman, and D. Hooi Ting, "Esports as playful
consumption experiences: Examining the antecedents and consequences of game
engagement," Telematics and Informatics, vol. 77, p. 101937, 2023/02/01/ 2023, doi:
https://doi.org/10.1016/j.tele.2023.101937.
[35] M. A. Rahaman, H. M. K. Hassan, A. Al Asheq, and K. M. A. Islam, "The interplay between
eWOM information and purchase intention on social media: Through the lens of IAM and
TAM theory," PLoS ONE, Article vol. 17, no. 9 September, 2022, Art no. e0272926, doi:
http://doi.org/10.1371/journal.pone.0272926.
[36] E. Ardyan, D. Kurniawan, I. Istiatin, and L. Luhgiatno, "Does customers’ attitude toward
negative eWOM affect their panic buying activity in purchasing products? Customers
satisfaction during COVID-19 pandemic in Indonesia," Cogent Business and Management,
Article vol. 8, no. 1, 2021, Art no. 1952827, doi:
https://doi.org/10.1080/23311975.2021.1952827.
[37] O. Gutierrez-Aguilar, F. Ticona-Apaza, Y. Tomaylla-Quispe, and V. Ticona-Apaza, "Mediating
role of influencers in behavior towards brands," Revista Venezolana de Gerencia, Article vol.
28, no. 9, pp. 613-627, 2023, doi: 10.52080/rvgluz.28.e9.38.
[38] C. Fornell and D. F. Larcker, "Evaluating structural equation models with unobservable
variables and measurement error," Journal of marketing research, vol. 18, no. 1, pp. 39-50,
1981.
[39] J. A. MARTÍNEZ-GARCÍA and L. MARTÍNEZ-CARO, "La validez discriminante como criterio
de evaluación de escalas: ¿teoría o estadística?," Universitas Psychologica, vol. 8, pp. 27-36,
2009. [Online]. Available:
http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S1657-
92672009000100002&nrm=iso.
[40] J. P. B. Kosiba, R. Odoom, H. Boateng, K. K. Twum, and I. K. Abdul-Hamid, "Examining
students’ satisfaction with online learning during the Covid-19 pandemic - an extended
UTAUT2 approach," Journal of Further and Higher Education, vol. 46, no. 7, pp. 988-1005,
2022/08/09 2022, doi: http://doi.org/10.1080/0309877X.2022.2030687.