Bodhi
Haryanto1, Ahlam Jarullah Alshoushan2*
1,2Universitas Sebelas Maret, Surakarta, Indonesia
Email:
budhiharyanto@yahoo.com, ahlamelshoshan@gmail.com
Abstract: The internet penetration rate has shown an average user growth rate of
62.5% per year. As of 2021, the number of internet users has reached approximately
4.9 billion, and social network users were around 3.8 billion, compared to
about 16 million internet users in 1995. The aim of this research is to test
the influence of digital marketing on innovative performance and the role of
knowledge sharing as a mediating variable, with a specific focus on the
Corinthia Hotel in Tripoli. The paradigm chosen by the researchers in this
study is post-positivism, and the method used is quantitative research. The
research sample consists of managers and staff of five-star hotels,
specifically from the Corinthia Hotel. A total of 160 respondents were
selected, all of whom are managers and staff from this hotel. This study uses
SmartPLS software to analyze the data. The results show that digital marketing
positively affects knowledge sharing, knowledge sharing positively influences
innovative performance, digital marketing positively impacts innovative
performance, and knowledge sharing mediates the relationship between digital
marketing and innovative performance. These findings highlight that the impact
of digital marketing on innovation is amplified when effective knowledge
sharing is facilitated. Since the study focuses on a single hotel, the findings
should be interpreted as a case study and may not be generalized to the broader
industry.
Keywords: Digital Marketing,
Innovative Performance, Knowledge Sharing.
INTRODUCTION
The rapid growth of internet
penetration, with an average user growth rate of 62.5% per year, has
transformed the global digital landscape. By 2021, the number of internet users
had reached approximately 4.9 billion, and social network users totaled around 3.8 billion, compared to just 16 million
internet users in 1995. This growth has been driven by factors such as the
proliferation of smart devices, technological advancements, and the expansion
of internet infrastructure worldwide. In particular, digital transformation has
become a necessity in both the public and private sectors, including the
hospitality industry, where hotels increasingly adopt digital tools to enhance
operational efficiency and customer engagement
In the context of five-star hotels,
e-business has emerged as a cornerstone of digital transformation. Hotels
leverage electronic systems for online reservations, personalized marketing,
and revenue management. Advanced booking engines and customer relationship
management (CRM) systems enable hotels to optimize room occupancy, tailor guest
experiences, and enhance loyalty through targeted services. Furthermore, the
adoption of digital marketing strategies—such as social media marketing, search
engine optimization (SEO), and content marketing—empowers hotels to engage with
specific market segments, analyze customer feedback,
and develop innovative approaches to service delivery
Digital marketing also serves as a
platform for knowledge sharing, which is critical for fostering innovation in
luxury hotels. Through collaborative tools, real-time data exchange, and
employee training programs, hotels can create a culture of knowledge sharing
that enhances service quality and supports innovative performance. Knowledge
sharing not only facilitates the transfer of expertise and best practices
across departments but also empowers employees to contribute creative ideas
that address evolving market demands and guest preferences
In this context, innovative
performance refers to a hotel's ability to introduce new products, services,
and processes that differentiate it from competitors while delivering added
value to customers. By leveraging insights gained through digital marketing,
hotels can anticipate guest needs, refine service offerings, and adopt
data-driven decision-making strategies that drive innovation. For instance, the
integration of digital marketing analytics with operational processes enables
hotels to tailor experiences to individual guests and implement dynamic pricing
strategies that maximize revenue potential
Previous studies have established a
positive relationship between digital marketing and innovative performance in
the hospitality sector. For example, Zhu and Gao
This study seeks to build upon these
findings by analyzing the role of knowledge sharing
in mediating the influence of digital marketing on innovative performance in
five-star hotels. By examining this relationship, the research aims to
contribute to a deeper understanding of how digital marketing strategies and
knowledge-sharing practices can enhance innovation in the competitive landscape
of luxury hospitality. The purpose of this study is to analyze
the role of knowledge sharing in mediating the influence of digital marketing
on innovative performance in five-star hotels.
MATERIALS AND METHODS
The paradigm chosen by the researchers
in this study is post-positivism. Post-positivism is used because positivism is
the paradigm from which postpositivism originates
Table 1. Conceptual And Operational
Definitions of Variables
Variables |
Conceptual Definition |
Operational Definitions |
Source |
Knowledge Sharing |
Tupamahu |
1. The knowledge shared by
employees in Corinthia Hotel is relevant to the topics. 2. The knowledge shared by
employees in Corinthia Hotel is easy to understand. 3. The knowledge shared by
employees in Corinthia Hotel is accurate. 4. The knowledge shared by
employees in Corinthia Hotel is complete. 5. The knowledge shared by
employees in Corinthia Hotel is reliable. 6. The knowledge shared by
employees in Corinthia Hotel is timely. |
Lee |
Digital Marketing |
Digital marketing is an
activity in the field of marketing that utilizes platforms on the internet to
reach target consumers (Sukma, 2020). |
1. Assess the importance
that digitalization can have on Corinthia Hotel. 2. Corinthia
Hotel has a Digital Transformation strategy. 3. Corinthia
Hotel identifies opportunities promoted by digital technologies. 4. Learn about the tools
available to digitize your business. 5. I have sufficiently
trained personnel dedicated to the digitization of Corinthia Hotel. 6. Corinthia
Hotel culture values the digitization of Corinthia Hotel. 7. Through digital
technologies, Corinthia Hotel identifies the level of employee engagement with the
roles they perform. 8. Corinthia
Hotel considers that teleworking favors the development of its activity |
Ramírez et al. |
Innovative Performance |
Innovation performance
is actually one of the most important dynamics that allows companies to
achieve a high level of competitiveness in both national and international
markets |
1. Corinthia
Hotel contributes to the commercialization of new products. 2. Corinthia
Hotel contributes to the introduction of new or improved products and/or
services in the market. 3. Corinthia
Hotel is concerned about introducing improvements in products and/or services.
4. Corinthia
Hotel is concerned with implementing new processes that reduce the
manufacturing cycle or improve production flexibility. |
Ramírez et al. |
This research uses an interval scale
which can describe the separation between two data. The Likert scale is part of
the Ordinal scale in this research. The Likert scale is used to measure the
attitudes, views, and perceptions of a person or group toward social phenomena.
The questionnaire distributed in this study used a Likert scale using a 5-point
scale (1-5): 1 is Strongly Disagree; 2 is Disagree; 3 is Neutral; 4 is Agree; 5
is Strongly Agree. The study will employ a survey sample approach to collect
responses from marketing managers and operational managers of five-star hotels,
with a specific focus on the Corinthia Hotel. The survey instrument is intended
to gather pertinent data on several aspects, including innovative performance
indicators, information-sharing habits, and digital marketing strategies. The
tool used in this research is a questionnaire.
The researcher's next step is to
choose an approach for data analysis. Preparing data for analysis and
evaluating data quality are the first two steps that must be taken before
choosing a data analysis technique. There are several steps in collecting data
for analysis and determining its accuracy. Researchers will explain the
approach used in this research, namely Partial Least Square (PLS).
RESULTS AND DISCUSSION
Table 2. Respondent Profile
Gender |
n |
% |
Female |
82 |
39,0 |
Male |
128 |
61,0 |
Age |
n |
% |
<30 years |
108 |
51,4 |
> 50 years |
15 |
7,1 |
31 – 40 years |
66 |
31,4 |
41 – 50 years |
21 |
10,0 |
Education |
n |
% |
Bachelor Degree |
99 |
47,1 |
Diploma |
86 |
41,0 |
Master Degree |
25 |
11,9 |
Work |
n |
% |
Government employees |
46 |
21,9 |
Private sector employees |
164 |
78,1 |
Total |
210 |
100 |
Based on Table 2, it is known that
61.0% (128 respondents) identified as male and 39.0% (82 respondents)
identified as female, and the sample is mostly male. In terms of age, 51.4%
(108 respondents) of the sample are under 30 years old, which is the largest
group of respondents. The next biggest group is those between the ages of 31
and 40, who make up 31.4% of the sample (66 respondents), followed by people
between the ages of 41 and 50 (10.0%), and people above 50 (15 respondents),
who make up just 7.1%. Regarding educational background, 47.1% of respondents
(99 respondents) have a bachelor's degree, 41.0% have a diploma (86
respondents), and 11.9% have a master's degree (25 respondents). Regarding the
employment sector, 78.1% (164 respondents) of the respondents work in the
private sector, while 21.9% (46 respondents) are employed by the government.
Table 3. Descriptive Results
N |
Minimum |
Maximum |
Mean |
Std. Deviation |
|
Knowledge Sharing |
|||||
KS1 |
210 |
3.00 |
5.00 |
4.3429 |
0.20821 |
KS2 |
210 |
3.00 |
5.00 |
4.3429 |
0.18414 |
KS3 |
210 |
3.00 |
5.00 |
4.3810 |
0.15992 |
KS4 |
210 |
3.00 |
5.00 |
4.4333 |
0.13386 |
KS5 |
210 |
1.00 |
5.00 |
4.3667 |
0.32800 |
KS6 |
210 |
3.00 |
5.00 |
4.4571 |
0.10884 |
Digital Marketing |
|||||
DM1 |
210 |
3.00 |
5.00 |
4.4238 |
0.12352 |
DM2 |
210 |
3.00 |
5.00 |
4.4381 |
0.17747 |
DM3 |
210 |
4.00 |
5.00 |
4.5143 |
0.10099 |
DM4 |
210 |
4.00 |
5.00 |
4.5143 |
0.10099 |
DM5 |
210 |
2.00 |
5.00 |
4.3762 |
0.31645 |
DM6 |
210 |
4.00 |
5.00 |
4.5524 |
0.09844 |
DM7 |
210 |
3.00 |
5.00 |
4.4952 |
0.13801 |
DM8 |
210 |
3.00 |
5.00 |
4.4476 |
0.10795 |
Innovative Performance |
|||||
IP1 |
210 |
3.00 |
5.00 |
4.5190 |
0.11030 |
IP2 |
210 |
2.00 |
5.00 |
4.3429 |
0.28236 |
IP3 |
210 |
3.00 |
5.00 |
4.3857 |
0.18595 |
IP4 |
210 |
3.00 |
5.00 |
4.3476 |
0.24750 |
Based on Table 3, it can be seen that
the mean scores for information sharing vary from 4.3429 to 4.4571, with all
items displaying comparatively high values, suggesting that most respondents
concur with the assertions regarding knowledge sharing. With the greatest mean
score (4.4571) and the lowest standard deviation (0.10884), item KS6 appears to
be the one that is most frequently agreed upon. However, while having a high
mean of 4.3667, KS5 has the highest standard deviation (0.32800), suggesting a
somewhat higher variety in responses.
All of the entries in the Digital
Marketing category have high mean ratings, ranging from 4.3762 to 4.5524. With
the highest mean score (4.5524) and the lowest standard deviation (0.09844),
item DM6 shows that respondents strongly believe that digital marketing is
important. Despite having a high mean of 4.3762, item DM5 has the largest
standard deviation (0.31645), indicating a higher degree of response
variability.
Most items show favorable
answers, and the mean ratings for Innovative Performance vary from 4.3429 to
4.5190. With the greatest mean score (4.5190), IP1 demonstrates that
respondents strongly concur with the claims regarding inventive performance.
IP2 has the biggest standard deviation (0.28236) among these items, suggesting
that responses to this item are more diverse than those to other items in this
category.
Analysis of Measurement Model Results in
Actual Research
Figure 2. Outer Model
Actual Test Validity Test
The convergent validity of the
pre-test was checked using Average Variance Extracted (AVE) and Outer Loading.
The test was carried out in accordance with the guidelines of Ghozali and Latan (2015), which stipulate that a variable
will be considered valid if, as a general rule, the Outer Loading (Standardized Loading Estimate) of an
indicator has a value greater than 0.70 and, just as a general rule, AVE must
have a value greater than 0.50. Additionally, verified that for the AVE to be
considered valid, its value in the convergent validity test must be greater
than 0.5. The confirmatory factor analysis test produced the following
findings, which are valid because the value is higher than 0.5. So, all the
indicators in this research are valid.
Table 4. Convergent Validity and Reliability
Test
Variable / Indicator |
Outer Loading |
Composite Reliability |
Cronbach's Alpha |
AVE |
|
Knowledge Sharing |
0.841 |
0.881 |
0.739 |
||
KS1 |
0.890 |
|
|
||
KS2 |
0.772 |
|
|
||
KS3 |
0.910 |
|
|
||
KS4 |
0.853 |
|
|
||
KS5 |
0.837 |
|
|
||
KS6 |
0.851 |
|
|
||
Digital Marketing |
0.846 |
0.935 |
0.689 |
||
DM1 |
0.762 |
|
|
||
DM2 |
0.799 |
|
|
||
DM3 |
0.841 |
|
|
||
DM4 |
0.867 |
|
|
||
DM5 |
0.830 |
|
|
||
DM6 |
0.858 |
|
|
||
DM7 |
0.822 |
|
|
||
DM8 |
0.854 |
|
|
||
Innovative Performance |
0.819 |
0.925 |
0.728 |
||
IP1 |
0.879 |
|
|
||
IP2 |
0.897 |
|
|
||
IP3 |
0.797 |
|
|
||
IP4 |
0.861 |
|
|
From the data above, it is known that
all indicators have an outer loading value greater than 0.6, which shows the
validity of the indicators. The convergent validity test using Average Variance
Extraction was carried out after the convergent validity test used confirmatory
factor analysis. Based on the data above, all variables are valid because the
extracted Average Variance Extracted (AVE) value is more than 0.5.
Using Composite Reliability and
Cronbach's Alpha, a reliability test was then carried out. Based on the test
findings, all variables in this study can be considered credible because the
results of Composite Reliability are above the threshold of 0.6, and Cronbach's
Alpha is above the threshold of 0.7. This research will carry out real testing
with the actual number of samples after this preliminary testing is completed
in order to carry out a statistical analysis of the relationship between
variables.
Table 5. Discriminant Validity
Variable |
Digital Marketing |
Innovative Performance |
Knowledge Sharing |
Digital Marketing |
|
|
|
Innovative Performance |
0.837 |
|
|
Knowledge Sharing |
0.804 |
0.955 |
|
Variables that have HTMT values below
0,9 and can be declared valid. The results of this study show HTMT values that
are less than 0,9, which means that the discriminant validity is good.
Structural Model Results in Actual Research
GoF Model Testing
The GoF
index was computed in order to test the model. The following formula may be
used to get the GoF index: √AVE × R2 = GoF. Table 4.5 displays each indicator's AVE and R2 values
along with their averages. A minor portion was not included in the
bootstrapping analysis despite the fact that the study data testing revealed
many indications that satisfied the validity and reliability requirements. The
goodness-of-fit model is tested first, and the findings show that the GoF value is equal to 0.805. According to Table 4.5, the
model fits well.
Table 6. Coefficient of Determination Results
(R2)
Variable |
AVE |
R square |
GoF |
Digital Marketing |
0.689 |
|
|
Knowledge Sharing |
0.739 |
0.873 |
|
Innovative Performance |
0.728 |
0.930 |
|
Model fit |
|
|
0.805 |
Note:
GoF = √AVE x R2
Based on Table 6 above, it can be seen
that the knowledge-sharing variable can be explained by the digital marketing
and innovative performance variables as much as 87.3%, and the rest was
influenced by other variables not examined in this study. Then, the innovative
performance variable can be explained by this research variable as much as 93%,
and the rest was influenced by other variables not examined in this study.
Figure 3. Inner Model(Bootstrapping)
Path Coefficients Test (Hypothesis Test)
Table 7. Path Coefficients Results
|
|
Path Coefficients |
Q statistics |
Rules of thumb |
P Values |
Information |
H1 |
Digital marketing à knowledge sharing |
0.934 |
59.489 |
>1.655 |
0.000*** |
Accepted |
H2 |
Knowledge Sharing à Innovative Performance |
0.558 |
5.143 |
0.000*** |
Accepted |
|
H3 |
Digital Marketing àInnovative Performance |
0.422 |
4.021 |
0.000*** |
Accepted |
|
H4 |
Knowledge Sharing can
mediate the influence of Digital Marketing on Innovative Performance |
0.522 |
5.074 |
0.000*** |
Accepted |
Digital Marketing Has a Positive Effect on
Knowledge Sharing
The results of actual research data
processing in Table 4.10 show that the p-value in the first hypothesis is 0.000
< 0.05, meaning that the first hypothesis in this research is accepted and
Digital marketing has a positive effect on knowledge sharing.
Knowledge sharing benefits from
digital marketing for several important reasons, including accessibility,
engagement, and the efficiency of information distribution. To reach a large
audience, digital marketing uses various online channels, including social
media, websites, email, and other digital platforms. This makes it possible for
information to spread without respect to physical barriers across demographic
and geographic places
Blogging and content marketing
strategies provide in-depth articles and tutorials that enhance knowledge and
expertise on certain topics. Through widespread accessibility, SEO tactics
increase the material's readership and impact. Subject matter experts and their
viewers can have direct conversations via interactive online forums, podcasts,
and webinars that offer real-time information sharing and interaction. Email
newsletters provide subscribers with a customized means of receiving content
that has been carefully chosen to keep them informed and engaged. By applying
analytics tools in digital marketing to research customer preferences and
habits, content creators may improve their strategies to provide more useful
and relevant information.
Knowledge Sharing Has a Positive Effect on
Innovative Performance
The results of actual research data
processing in Table 4.10 show that the p-value in the first hypothesis is 0.000
< 0.05, meaning that the second hypothesis in this research is accepted and
Knowledge Sharing has a positive effect on Innovative Performance.
People can access a range of thoughts,
viewpoints, and experiences from other team members or companies through
knowledge sharing. Through the presentation of fresh ideas and methods that
would not have surfaced in a more constrained or homogeneous setting, this
diversity of viewpoints can foster creativity and innovation
Knowledge sharing fosters a
collaborative environment that permits information and ideas to flow freely,
enhancing creativity and problem-solving abilities and positively influencing
innovative performance. By pooling their diverse experiences and points of
view, individuals and organizations may create new ideas and improve existing
practices via the sharing of information. This collaborative culture fosters
constant learning and adaptation, which is necessary for innovation. Knowledge
sharing also accelerates innovation by removing redundancies and enhancing
resource efficiency. Rather than beginning from scratch, teams may focus on
more complicated and creative aspects of projects by utilizing pre-existing
experience. Additionally, having access to a broad range of experiences and
information that support identifying novel trends and opportunities facilitates
proactive innovation techniques
Digital Marketing Has a Positive Effect on
Innovative Performance
The results of actual research data
processing in Table 4.10 show that the p-value in the first hypothesis is 0.000
< 0.05, meaning that the third hypothesis in this research is accepted and
Digital Marketing has a positive effect on Innovative Performance.
Digital marketing offers strong
analytical capabilities for gathering and analyzing
data on customer behavior, industry trends, and
campaign effectiveness. Insights into consumer preferences and market dynamics
are provided by this data, which may be utilized to spot possibilities for
innovation and create more winning plans of action
Using digital marketing helps
businesses get important consumer data, test and refine new ideas fast, and
grow their client base. Digital marketing, therefore, has a positive effect on
creative performance. Companies may use social media, email marketing, and
internet advertising to obtain real-time feedback and information about the
tastes and habits of their clients. This information is crucial for producing
innovative products and services that meet consumer demand
Knowledge Sharing Can Mediate the Influence of
Digital Marketing on Innovative Performance
The results of actual research data
processing in Table 4.10 show that the p-value in the first hypothesis is 0.000
< 0.05, meaning that the fourth hypothesis in this research is accepted and
Knowledge Sharing can mediate the influence of Digital Marketing on Innovative
Performance.
Data on customer behavior,
market trends, and campaign efficacy are produced by digital marketing. This
increases the consistency and relevance of innovation by giving various teams
access to the insights required to put creative initiatives based on the same
data into practice. A collaborative understanding of digital marketing tactics
and campaign outcomes may facilitate the comprehension and execution of more
effective team plans. Innovation teams may create new ideas that are more
relevant to market demands and more effective when information about
cutting-edge technology or successful marketing methods is shared. Through this
collaboration, diverse concepts and information may be integrated to provide
creative solutions.
The effect of digital marketing on
inventive performance can be lessened through knowledge sharing, which acts as
a conduit for the internal distribution of data gathered from these projects.
Digital marketing generates a lot of data on consumer preferences, market
trends, and competition. Kanaan et al.
CONCLUSION
The first hypothesis in this research
is accepted, indicating that digital marketing positively affects knowledge
sharing. This underscores the critical role of digital marketing in fostering
the exchange of information and knowledge within organizations. The second
hypothesis is also accepted, demonstrating that knowledge sharing positively
influences innovative performance. This finding suggests that the effective
exchange of knowledge directly enhances an organization's capacity to innovate.
Furthermore, the third hypothesis is accepted, showing that digital marketing
has a positive effect on innovative performance. This indicates that
organizations implementing digital marketing strategies can achieve better
innovation outcomes. Finally, the fourth hypothesis is confirmed, establishing
that knowledge sharing mediates the relationship between digital marketing and
innovative performance. This highlights the importance of knowledge sharing in
amplifying the impact of digital marketing on innovation.
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