The Influence of Digital Marketing on Innovative Performance with Knowledge Sharing as a Mediation Variable in Five Star Hotels (Case Study Corinthia Hotel)

 

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 (Zachlod et al., 2022).

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 (Dwivedi et al., 2021).

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  (B.-L. Chua et al., 2020; Feng et al., 2021).

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  (Dimitrios et al., 2023; Makrides et al., 2020).

Previous studies have established a positive relationship between digital marketing and innovative performance in the hospitality sector. For example, Zhu and Gao (2019) highlighted how digital marketing capabilities enable luxury hotels to differentiate themselves from competitors and improve service quality. Similarly, Kim and Ko (2020) demonstrated the mediating role of knowledge sharing in strengthening the relationship between digital marketing and innovation, emphasizing the importance of fostering a knowledge-sharing culture within organizations.

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 (Maksimovic & Evtimov, 2023). Quantitative research is used because this research process is calculated numerically. These data will later be evaluated to see their relationship to the research objectives so that conclusions can be drawn from further findings. To determine the magnitude of the influence of the variable (Lehmann, 2023). The population used in this research were operational managers and marketing managers of Five Star Hotels. The sampling technique used in this research was the purposive sampling method. The selection of the purposive sampling strategy was based on its compatibility with the particular goals and features of the research population. Purposive sampling enables researchers to specifically choose participants who have the necessary knowledge, skills, and experience (Campbell et al., 2020). This research sample is managers and staff of five-star hotels, with a focus on the Corinthia Hotel. The number of samples used in this research was 160 respondents, managers and staff of Five Star Hotels   (Corinthia Hotel).

Table 1. Conceptual And Operational Definitions of Variables

Variables

Conceptual Definition

Operational Definitions

Source

Knowledge Sharing

Tupamahu (2020) states that knowledge sharing is the behavior of individuals sharing what they have learned and transferring what they know to those who have common interests and have found useful knowledge.

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 (2018)

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. (2019)

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 (Agustina & Arganata, 2023).

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. (2019)

 

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 (Coroiu et al., 2020). Blog posts, infographics, and video lessons, for instance, are easily accessible to people worldwide and facilitate the efficient dissemination of knowledge to a large audience. Direct communication between content providers and viewers is made possible by digital platforms like social media and online forums. Feedback, inquiries, and discussions on the material are all welcome from the audience, which opens up possibilities for future information development and clarification. When audiences actively participate in conversations, understanding and retention of the material are increased.

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 (R. Y. J. Chua, 2018). Open communication of knowledge facilitates departmental or team collaboration. Through this cooperation, diverse ideas and skill sets may be integrated, resulting in synergies that stimulate the creation of creative solutions. Teams that collaborate and have integrated knowledge are better able to find innovative solutions to challenges.

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 (Lee, 2018).

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 (Troisi et al., 2020). Digital marketing strategies, including market segmentation, social media analytics, and online surveys, help businesses better understand the requirements and preferences of their target audience. With this information, businesses may create more inventive and useful goods and services that cater to consumers' actual requirements and preferences.

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 (Kanaan et al., 2023). Through the analytics tools of digital marketing platforms, businesses can track the effectiveness of their campaigns and identify which ones connect with and turn off their target audience. This repeated process of testing and learning accelerates the innovation cycle, enabling companies to make data-driven decisions and swiftly adapt to changing market conditions. Digital marketing also promotes collaboration and information sharing between various industries and enterprises. The exchange of best practices and concepts on websites like LinkedIn, seminars, and online forums inspires new concepts and solutions. In an environment of cooperation, creativity must be encouraged (Jung & Shegai, 2023).

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. (2023) claim that effective distribution of this data among teams fosters a collaborative environment where insights may be transformed into original ideas and solutions. By using data from digital marketing, organizations may identify emerging trends and possibilities. You can make sure that everyone is on the same page and can innovate together by sharing these ideas throughout departments. Because of this shared understanding, teams are better able to develop more targeted and creative plans, which also enhances overall innovative performance. Moreover, knowledge exchange promotes a culture of continuous learning and adaptability. Teams may exchange knowledge about successful digital marketing methods and customer feedback while refining existing tactics and testing out new ideas. By employing this iterative process, which is crucial for sustainable innovation, organizations can preserve their competitive edge.

 

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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© 2024 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY SA) license (https://creativecommons.org/licenses/by-sa/ 4.0/).