







































X. Liang et al. /Future Technology                                                                                         August 2025| Volume 04 | Issue 03 | Pages 
138-147 

138 

 

 

 

Article 

Digital marketing integration and educational 

product innovation: the mediating effect of 

organizational innovation climate 
Xinrui Liang1,2, Wan Mohd Hirwani  Wan Hussain1*, Rabiah Abdul Kadir2  

1Graduate School of Business, Universiti Kebangsaan Malaysia 43600 UKM Bangi Selangor, Malaysia 
2Institut Informatik Visual (IVI), Universiti Kebangsaan Malaysia 43600 UKM Bangi Selangor, Malaysia 

A R T I C L E   I N F O 
 

Article history: 
Received 14 April 2025  
Received in revised form 
26 May 2025 
Accepted 07 June 2025 
 
Keywords:  
Digital marketing, Educational innovation, 
Organizational innovation climate, AI marketing, 
Malaysian educational institutions 
 
*Corresponding author 
Email address: 
wmhwh@ukm.edu.my 
 
 
DOI: 10.55670/fpll.futech.4.3.13 

A B S T R A C T 
 

This study investigates the relationships between digital marketing strategies 
(social media marketing, video marketing, and artificial intelligence marketing), 
organizational innovation climate, and product innovation performance in 
Malaysian educational institutions, focusing on the mediating effect of 
organizational innovation climate. A quantitative cross-sectional survey design 
is employed, collecting data from 169 employees working in Malaysian 
educational institutions, including administrative staff, marketing personnel, 
academic leaders, and innovation team members from both public and private 
institutions. The research model is tested using Partial Least Squares Structural 
Equation Modeling (PLS-SEM). The findings reveal that all three dimensions of 
digital marketing positively impact organizational innovation climate, with 
artificial intelligence marketing demonstrating the strongest effect (β = 0.323), 

followed by video marketing (β = 0.289) and social media marketing (β = 

0.247). Organizational innovation climate significantly influences product 
innovation performance (β = 0.683). While social media marketing and video 
marketing exhibit both direct and indirect effects on innovation performance, 
artificial intelligence marketing operates entirely through organizational 
innovation climate, indicating full mediation. The results suggest that 
educational institutions should implement advanced digital marketing tools 
alongside nurturing organizational structures that support innovation, with 
artificial intelligence marketing investments requiring simultaneous 
development of innovation-friendly climates. Strategic digital marketing 
significantly impacts educational product innovation through organizational 
innovation climate, enabling institutions to adapt to emerging insights and 
design innovative educational products tailored to student demands. 

1. Introduction 

As an outcome of the ongoing changes in the spheres of 
innovation and technology, the application of marketing tools 
and concepts in educational institutions has gained 
significance in enhancing competitiveness and innovation. 
The problem of the organizational innovation model and the 
concepts and techniques of digital marketing intersection is 
very important but still remains insufficiently researched, 
particularly in regard to educational innovation [1, 2]. As 
educational institutions strive to enhance their 
competitiveness and foster innovation capabilities, there is an 
urgent need to understand how digital marketing tools and 
organizational factors interact to drive educational product 
innovation. Malaysian educational institutions face significant 
challenges in their pursuit of becoming regional hubs for 

international education. Despite substantial investments in 
digital technologies and marketing initiatives, many 
institutions struggle to effectively translate these investments 
into tangible innovation outcomes. The primary challenge lies 
in understanding how different dimensions of digital 
marketing—specifically social media marketing, video 

marketing, and artificial intelligence (AI) marketing—
contribute to educational product innovation. Furthermore, 
the role of organizational innovation climate as a potential 
mediating mechanism between digital marketing strategies 
and innovation performance remains unclear. This 
knowledge gap is particularly problematic for Malaysian 
educational institutions, which must navigate the complex 
dynamics of digital transformation while maintaining their 
competitive positioning in an increasingly saturated market. 

Open Access Journal 

 

 

ISSN 2832-0379 

August 2025| Volume 04 | Issue 03 | Pages 138-147 

https://doi.org/10.55670/fpll.futech.4.3.13 

 

 

 

Journal homepage: https://fupubco.com/futech 

 

Future Technology 

Open Access Journal 

mailto:wmhwh@ukm.edu.my
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X. Liang et al. /Future Technology                                                                                         August 2025| Volume 04 | Issue 03 | Pages 138-147 

139 

 

The implementation of marketing technologies such as social 
media, video advertisements, and AI tools enables 
educational institutions to effectively engage with 
prospective students, gather data, and market differentiate 
their offerings [3]. These strategies go beyond promotion and 
serve to fully understand students, engage with them in co-
creating their educational paths, and develop responsive 
solutions that address market needs. As pointed out by Paños-
Castro et al. [4], the consequences of digital transformation on 
an institution go beyond technological integration, including 
substantially more profound alterations to the organizational 
culture and innovation ecosystems. This change is crucial for 
Malaysia's education industry as institutions are trying to 
brand themselves as regional hubs for international 
education. 

The impact of digital marketing on organizational 
processes becomes evident through its influence on 
structural and cultural components. Organizational 
innovation climate, defined as the shared perceptions within 
an organization regarding policies, behaviors, and actions 
that support and encourage innovation, emerges as a crucial 
mediating variable in this relationship [5]. Such a climate 
enables organizations to effectively capitalize on market 
knowledge acquired through digital marketing channels by 
transforming it into concrete product innovations. As noted 
by Kim et al. [6], a stronger organizational innovation climate 
tends to amplify the influence of external inputs, including 
market feedback captured through digital means, on 
innovation outcomes. The investigation of how organizational 
structural components relate to digital marketing adoption as 
institutional policy presents a compelling perspective for 
examining how innovation outcomes can be enhanced in 
higher education institutions. 

Despite the growing importance of digital marketing in 
educational contexts, the relationship between digital 
marketing strategies, organizational innovation climate, and 
educational product innovation performance has not been 
comprehensively investigated, particularly within the 
Malaysian educational marketplace. This research is aimed at 
examining the impact of digital marketing elements—namely, 
social media marketing, video marketing, and AI-driven 
marketing—on educational product innovation through the 

mediating role of organizational innovation climate. 
Specifically, this study seeks to examine the direct impact of 
these digital marketing strategies on educational product 
innovation performance, investigate their influence on 
organizational innovation climate, analyze the mediating role 
of organizational innovation climate in these relationships, 
and identify the differential effects of various digital 
marketing dimensions on innovation outcomes.  

The scope of this research is focused on Malaysian 
educational institutions with the hope of enhancing both the 
conceptual framework and actionable recommendations 
towards the application of digital marketing to foster 
innovation in the education sector. The findings would be of 
great importance to educational institutions struggling to 
undergo a digital transformation while maintaining a 
strategic position characterized by continuous innovation. By 
elucidating the complex relationships between digital 
marketing strategies, organizational factors, and innovation 
outcomes, this research contributes to the broader discourse 
on educational innovation and digital transformation in 
emerging market contexts. 

 
 

2. Literature review 

2.1 Digital marketing strategies in educational 
institutions 
The shift in technology and the perception of students 

has greatly changed the marketing landscape of educational 
institutions. The term digital marketing strategies 
encompasses a wide variety of methods used to connect with 
target audiences and improve an organization’s 

productivity using online tools. In education, these strategies 
have become more sophisticated, involving extensive 
promotional engagements and stakeholder activities [5]. As a 
result, social media has become one of the leading marketing 
channels for educational institutions to build their presence 
and interact with prospective students. Effectively 
overcoming social media marketing challenges enables 
educational institutions to tailor their engagement with 
specific segments of their audience, particularly international 
students seeking courses [6]. Video marketing serves as an 
additional important aspect of digital marketing within an 
educational framework. Storytelling visually not only allows 
institutions to highlight their facilities and programmes but 
also enables them to showcase student activities in a more 
engaging way. Bustard et al. [7] emphasize that the 
application of design thinking with digital videos enables the 
creation of captivating experiences, which significantly 
impact student engagement and enrollment. The use of 
Artificial Intelligence (AI) has also transformed the approach 
to marketing within the educational field by providing 
targeted analytics, data-driven personalization, and 
forecasting. As demonstrated by Xiong et al. [8], the 
capabilities of digital integration, particularly those of AI, 
strengthen relationships between buyers and suppliers while 
facilitating product development through enhanced systems 
of information processing and decision-making. 

2.2 Organizational innovation climate and its role in 
education 
Organizational innovation climate represents the shared 

perceptions within an organization regarding policies, 
practices, and procedures that support and encourage 
innovative initiatives. In educational organizations, this 
climate has pronounced ramifications for how well 
organizations engage the market and technological 
innovation. Fischer & Riedl [9] study the strain potential of 
organizational innovation climate and attend to the intricate 
interrelations between the pressure for innovation and 
organizational productivity. Innovation climates tend to 
enhance creativity, but the findings indicate that they need to 
be managed to avoid stress that is counterproductive among 
faculty and staff. Newman et al. [10] further explain the 
organizational innovation climate by focusing on strategically 
important elements, such as faculty and leadership resource 
support, innovative team units, and inter-organizational 
collaboration, which influence innovation in an educational 
setting. Likewise, in their research, Li et al. [11] demonstrate 
that the learning climate of a team affects its innovation 
performance through the organization's capabilities for 
knowledge integration, thereby showing that educational 
institutions should foster systems that enable knowledge and 
learning to circulate continuously in order to enhance their 
innovative potential. 

2.3 Educational product innovation performance 
The effectiveness and efficiency with which an 

institution designs and executes new or upgraded educational 
offerings revolve around its educational product innovation 



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performance. For instance, Varadarajan et al. [12] focus on 
societal benefit-oriented digital product innovations. These 
innovations, as well as digital marketing innovations, are 
interdependent, especially within an educational framework. 
Successful educational product innovations tend to 
intertwine technological and marketing elements to fulfill 
shifting educational demands, as outlined by the authors. 

Within the scope of educational organizations, the 
criteria for measuring product innovation performance have 
expanded to include market engagement, student 
satisfaction, and operational efficiency. Shi et al. [13] discuss 
the interrelatedness of digital marketing and corporate 
innovation strategy, clarifying that innovation management 
plays a key role in strategic mediation. Their study illustrates 
that digital marketing combined with innovation 
management is far more effective than when both are treated 
independently. 

2.4 Theoretical framework and hypotheses 
development 
The theoretical foundation of this study integrates two 

complementary frameworks to explain the complex 
relationships between digital marketing strategies, 
organizational innovation climate, and product innovation 
performance. The Unified Technology Acceptance and Use 
Theory (UTAUT) offers insights into how marketing 
technologies are adopted and utilised by some educational 
institutions [14]. UTAUT provides valuable insights into the 
factors that influence the acceptance and utilization of digital 
marketing technologies, including performance expectancy, 
effort expectancy, social influence, and facilitating conditions. 
The organizational learning theory serves as the secondary 
theoretical lens, articulating how market-oriented insights 
obtained through digital marketing are systematically 
converted into innovative educational products through 
organizational learning processes [15]. This theory 
complements UTAUT by explaining the transformation 
mechanisms through which technological adoption translates 
into innovation outcomes. The integration of these theories 
creates a comprehensive framework where UTAUT explains 
the adoption and acceptance of digital marketing 
technologies, while organizational learning theory elucidates 
how organizations convert the insights gained from these 
technologies into concrete innovation outcomes through 
enhanced organizational innovation climate. 
Based on the literature review, the following hypotheses are 
formulated. 
H1: In educational institutions, digital marketing strategies 
impact product innovation performance positively. 
H2: Organizational innovation climate acts as a mediator in 
the relationship between digital marketing strategies and 
product innovation performance. 
H2a: Digital marketing strategies have a positive impact on 
organizational innovation climate. 
H2b: Organizational innovation climate has a positive impact 
on product innovation performance. 
H3: Of the three dimensions of digital marketing (social 
media marketing, video marketing, and AI marketing), each 
has a differential impact on the level of product innovation 
performance through organizational innovation climate. 

All these hypotheses form part of a single cohesive 
conceptual model. As demonstrated in Figure 1, the model 
depicts the proposed impact of digital marketing strategies, 
organizational innovation climate, and product innovation 
performance. It illustrates that the impact of digital marketing 
strategies extends to influencing product innovation 

performance both directly and indirectly through 
organizational innovation climate. 
 

 
Figure 1. Research hypotheses framework 

Figure 1 illustrates how the three facets of digital 
marketing, namely social media marketing, video marketing, 
and AI marketing, are predicted to shape innovation climate 
at the organizational level, which subsequently drives 
product innovation performance. This framework provides a 
coherent perspective for analyzing the intricate interplay 
among these constructs within Malaysian higher education 
institutions. 

3. Research methodology 

3.1 Research design and sample 
This study used a quantitative research approach with a 

cross-sectional survey design to investigate the associations 
among digital marketing, organizational innovation climate, 
and product innovation performance. This approach is 
consistent with the positivist epistemology approach, which 
focuses on the measurement of social phenomena and 
hypothesis testing. This research received ethical approval 
from the institutional review board to ensure compliance 
with ethical standards for human subjects research. The 
target population comprises employees in Malaysia's 
educational institutions that are involved in digital marketing 
and product innovation. The study's sampling frame 
encompassed the administrative and marketing staff, 
academic leaders, and innovation teams from both public and 
private educational institutions. 

The inclusion of diverse professional roles 
(administrative staff, marketing personnel, academic leaders, 
and innovation team members) as a unified sample is justified 
by their shared involvement in institutional digital marketing 
and innovation activities. While these groups may have 
different functional perspectives, they collectively contribute 
to the digital marketing ecosystem and innovation processes 
within educational institutions. Administrative staff provide 
insights into institutional policies and resource allocation, 
marketing personnel offer expertise in digital strategy 
implementation, academic leaders contribute perspectives on 
educational innovation needs, and innovation team members 
provide technical and creative insights. This multi-
stakeholder approach ensures comprehensive coverage of 
the digital marketing-innovation interface within educational 
institutions. 

To ensure respondents possessed relevant knowledge 
about their institutions' digital marketing and innovation 
activities, a purposive sampling method was applied. The 
minimum sample size was determined using G*Power 
analysis with parameters set at medium effect size (𝑓2 =
0.15), statistical power of 0.80, and significance level of 0.05, 
resulting in a minimum requirement of 127 respondents [16]. 
To account for potential non-response and incomplete 

                 

          

                      

               

            

              

                  

                    

                  

           

                    

      

  
               

                                                                    

                    



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responses, 210 questionnaires were distributed 
electronically using an online survey platform. After 
removing incomplete responses and outliers, 169 valid 
responses were retained for analysis, representing a response 
rate of 80.5%. 

3.2 Measurement instruments 
The survey instrument was developed based on 

established scales from previous studies, with necessary 
adaptations to fit the educational context. For the 
organizational innovation climate construct, measurement 
items were specifically adapted for the educational context 
through a systematic process involving literature review of 
education-specific innovation studies, consultation with 
education sector experts, and pilot testing with educational 
professionals. The adaptation process ensured that items 
captured the unique characteristics of innovation climate in 
educational settings, including academic freedom, 
collaborative research culture, and institutional support for 
pedagogical innovation. All items were measured using a five-
point Likert scale ranging from 1 (strongly disagree) to 5 
(strongly agree).  

Content validation for the educational context 
adaptations was conducted with a panel of eight experts 
comprising three academics specializing in educational 
innovation and five practitioners with extensive experience in 
educational institution management and digital marketing. 
The expert panel evaluated each measurement item for 
relevance, clarity, and appropriateness within the Malaysian 
educational context. The measurement of digital marketing 
strategies incorporated three dimensions based on the 
conceptual framework. The AI marketing construct was 
specifically developed for the educational context, 
incorporating items that reflect AI applications commonly 
used in educational institutions, such as chatbots for student 
inquiries, predictive analytics for enrollment management, 
personalized learning recommendations, and automated 
content curation for educational programs. For 
organizational innovation climate, the study adapted 
measurement items that capture organizational practices and 
procedures supporting innovation within educational 
institutions. Product innovation performance measurement 
focused on both the effectiveness and efficiency of innovation 
outcomes in the educational context. Table 1 presents the 
operational definitions and sample measurement items for 
each construct. 

3.3 Data collection procedure 
Data collection was conducted between January and 

March 2025 using a standardized online survey platform. 
Before distribution, the questionnaire was pre-tested with 
eight experts (three academics and five practitioners) to 
assess content validity, clarity, and comprehensiveness. Their 
feedback led to minor modifications in the wording of several 
items to enhance clarity. The revised questionnaire was then 
pilot-tested with a sample of 25 respondents to evaluate 
reliability. Cronbach's alpha values for all constructs 
exceeded the 0.70 threshold, indicating satisfactory internal 
consistency. 

To mitigate potential common method bias, several 
procedural and statistical remedies were implemented. 
Procedurally, the survey design included reverse-coded 
items, varied response formats where appropriate, and 
assured respondent anonymity to reduce social desirability 
bias. The questionnaire was structured to separate predictor 
and criterion variables temporally within the survey to 
minimize common method variance.  

Table 1. Constructs, measurement, and operational definitions 

Construct 
Operational 
Definition 

Sample Items 

Social Media 
Marketing 

(SMM) 

The strategic use 
of social media 

platforms to 
engage with 

stakeholders, build 
brand awareness, 

and promote 
educational 

services 

"Our institution 
regularly updates 

content on social media 
platforms". "We 

actively engage with 
user comments on our 
social media channels". 

Video 
Marketing 

(VM) 

The creation and 
distribution of 

video content to 
promote 

educational 
products and 

services 

"We create 
instructional videos to 

showcase our 
educational programs". 

"Our video content 
effectively 

communicates our 
institution's unique 
value proposition". 

AI Marketing 
(AIM) 

The application of 
artificial 

intelligence 
technologies to 

enhance 
marketing 

activities and 
personalize 

customer 
experiences 

"We use AI to analyze 
student data and 

personalize marketing 
messages". "Our 

institution employs 
chatbots to provide 

immediate responses to 
inquiries". 

Organizational 
Innovation 

Climate (OIC) 

The shared 
perceptions of 
organizational 

practices, 
procedures, and 
behaviors that 

support 
innovation 

"Leadership actively 
encourages new ideas 

and approaches". 
"Resources are readily 

available for 
implementing 

innovative projects". 

Product 
Innovation 

Performance 
(PIP) 

The effectiveness 
and efficiency with 

which an 
organization 
develops and 

implements new 
or improved 
educational 

products 

"Our new educational 
products/services have 
been well received by 

the market". "The 
innovation process in 

our institution is 
efficient in terms of 

time and resources". 

 
 
Additionally, Harman's single-factor test was conducted 

to assess the presence of common method bias, where all 
measurement items were loaded into an exploratory factor 
analysis to determine if a single factor accounts for the 
majority of variance. The survey was distributed to potential 
respondents via institutional email channels, with an 
introductory message explaining the research purpose, 
confidentiality assurances, and voluntary participation. To 
increase response rates, follow-up reminders were sent at 
two-week intervals. The survey included screening questions 
to ensure respondents had knowledge of their institutions' 
digital marketing strategies and innovation activities. 
Demographic information such as gender, age, job position, 
years of experience, and institutional type was also collected 
to enable sample characterization and potential control 
variable analysis. 

3.4 Data analysis techniques 
The collected data were analyzed using a two-step 

approach as suggested in methodological literature. First, the 
measurement model was assessed for reliability, convergent 



X. Liang et al. /Future Technology                                                                                         August 2025| Volume 04 | Issue 03 | Pages 138-147 

142 

 

validity, and discriminant validity using SmartPLS 3.0 
software. Second, the structural model was evaluated to test 
the hypothesized relationships. Partial Least Squares 
Structural Equation Modeling (PLS-SEM) was chosen as the 
analytical technique due to its suitability for complex models 
with multiple constructs and its robustness against non-
normality. 

For the measurement model, reliability was assessed 
using Cronbach's alpha, composite reliability (CR), and rho_A, 
with values above 0.70 considered acceptable. Convergent 
validity was evaluated using average variance extracted 
(AVE), with values exceeding 0.50 deemed satisfactory. The 
formula for AVE is as follows: 

2

1

n

i

iAVE
n


==
           (1) 

where 𝜆𝑖  represents the standardized factor loading, and n is 
the number of items. 
Discriminant validity was examined using the Fornell-
Larcker criterion and the Heterotrait-Monotrait (HTMT) 
ratio, with HTMT values below 0.85 indicating adequate 
discriminant validity. For the structural model, path 
coefficients (β), t-values, and p-values were calculated to 
assess the statistical significance of the hypothesized 
relationships. The coefficient of determination (𝑅2), effect 
size (𝑓2), and predictive relevance (𝑄2) were examined to 
evaluate the model's explanatory and predictive power. The  
effect size was calculated using the following formula: 

2 2
2

21

included excluded

included

R R
f

R

−
=

−

                                         (2) 

To test the mediating effect of organizational innovation 
climate, the bootstrapping procedure with 5,000 resamples 
was employed to estimate the significance of indirect effects. 
The specific indirect effect was calculated as the product of 
the path coefficients: 

indirect a b  =                                             (3) 

where 𝛽𝑎  represents the path from digital marketing 
strategies to organizational innovation climate, and 𝛽𝑏  
represents the path from organizational innovation climate to 
product innovation performance. The mediation analysis 
followed the approach recommended in current 
methodological literature, which focuses on the significance 
of indirect effects rather than the traditional step approach. 
This method provides a more robust assessment of mediation 
effects, particularly in complex models with multiple 
mediating pathways. 

4. Research results 

4.1 Respondent demographics 
The study achieved a response rate of 80.5%, with 169 

valid responses retained from the initial 210 distributed 
questionnaires. The demographic profile of respondents, as 
presented in Table 2, reveals a diverse sample representing 
various roles and institutions within Malaysia's educational 
sector. The gender distribution was relatively balanced, with 
53.8% male and 46.2% female participants. The majority of 
respondents (42.6%) fell within the 36-45 age group, 
followed by the 26-35 age range (28.4%). In terms of 
institutional representation, private educational institutions 
constituted the largest segment (57.4%), with public 
institutions accounting for 42.6% of the sample. Regarding 
job positions, 35.5% of respondents held administrative 

roles, while 26.6% were marketing personnel, 22.5% were 
academic leaders, and 15.4% were innovation team members. 
The distribution of work experience demonstrated that 
38.5% of respondents had 6-10 years of experience, 27.8% 
had 11-15 years, and 19.5% had more than 15 years, ensuring 
that the sample included professionals with substantial 
knowledge of institutional practices. 

Table 2. Demographic profile of respondents 

Characteristic Category Frequency 
Percentage 

(%) 

Gender 
Male 91 53.8 

Female 78 46.2 

Age 

18-25 15 8.9 

26-35 48 28.4 

36-45 72 42.6 

46-55 27 16.0 

Above 55 7 4.1 

Type of 
Institution 

Public 
Educational 
Institution 

72 42.6 

Private 
Educational 
Institution 

97 57.4 

Job Position 

Administrative 
Staff 

60 35.5 

Marketing 
Personnel 

45 26.6 

Academic 
Leader 

38 22.5 

Innovation 
Team Member 

26 15.4 

Years of 
Experience in 

Institution 

Less than 3 
years 

24 14.2 

3-5 years 33 19.5 

6-10 years 65 38.5 

11-15 years 47 27.8 

 
 

4.2 Measurement model assessment 
Prior to assessing the measurement model, common 

method bias was evaluated using Harman's single-factor test. 
The results indicated that no single factor accounted for the 
majority of variance (the largest factor explained 34.2% of the 
total variance), suggesting that common method bias was not 
a significant concern in this study. 

The reliability and validity of the measurement model 
were thoroughly assessed to ensure the robustness of the 
research instrument. As shown in Table 3, all constructs 
demonstrated satisfactory reliability, with Cronbach's alpha, 
composite reliability (CR), and rho_A values exceeding the 
recommended threshold of 0.70. Social Media Marketing 
(SMM) exhibited the highest internal consistency (α = 0.894, 

CR = 0.921), while AI Marketing (AIM) showed the lowest, 
though still acceptable, reliability values (α = 0.783, CR = 
0.851). 

 
 



X. Liang et al. /Future Technology                                                                                         August 2025| Volume 04 | Issue 03 | Pages 138-147 

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Table 3. Reliability and validity assessment 

Construct 
Number 
of Items 

Cronbach's 
Alpha 

rho_A 
Composite 
Reliability 

AVE 

SMM 4 0.894 0.898 0.921 0.745 

VM 4 0.876 0.879 0.915 0.730 

AIM 5 0.783 0.792 0.851 0.587 

OIC 6 0.862 0.867 0.897 0.594 

PIP 5 0.854 0.859 0.896 0.634 

Note: SMM = Social Media Marketing; VM = Video Marketing; AIM = 
AI Marketing; OIC = Organizational Innovation Climate; PIP = Product 
Innovation Performance; AVE = Average Variance Extracted. 

Convergent validity was confirmed by examining the 
Average Variance Extracted (AVE) values, all of which 
exceeded the 0.50 threshold, indicating that more than half of 
the variance in each construct was explained by its indicators. 
The highest AVE value was observed for Social Media 
Marketing (0.745), suggesting strong convergent validity for 
this construct. 

Discriminant validity was assessed using both the 
Fornell-Larcker criterion and the Heterotrait-Monotrait 
(HTMT) ratio. Table 4 presents the Fornell-Larcker criterion 
results, where the square root of AVE for each construct 
(shown in bold on the diagonal) exceeds its correlation with 
other constructs, confirming discriminant validity. 
Additionally, all HTMT ratios were below the conservative 
threshold of 0.85, further supporting the discriminant validity 
of the constructs (Table 5). 

Table 4. Fornell-larcker criterion 

Construct AIM OIC PIP SMM VM 

AIM 0.766     

OIC 0.586 0.771    

PIP 0.517 0.683 0.796   

SMM 0.429 0.542 0.495 0.863  

VM 0.468 0.578 0.538 0.617 0.854 

Note: Bold values on the diagonal represent the square root of AVE. 

Table 5. Heterotrait-monotrait (HTMT) ratio 

Construct AIM OIC PIP SMM VM 

AIM 0.766     

OIC 0.586 0.771    

PIP 0.517 0.683 0.796   

SMM 0.429 0.542 0.495 0.863  

VM 0.468 0.578 0.538 0.617 0.854 

 
4.3 Structural model assessment 

After confirming the reliability and validity of the 
measurement model, the structural model was evaluated to 
test the hypothesized relationships. Figure 2 illustrates the 
path coefficients and R² values of the path analysis model. As 

shown in Figure 2, the path diagram presents the 
relationships between the three dimensions of digital 
marketing (Social Media Marketing, Video Marketing, and AI 
Marketing), the mediating variable (Organizational 
Innovation Climate), and the dependent variable (Product 
Innovation Performance). The model displays both direct and 
indirect pathways, with solid lines representing the indirect 
effects through the mediating variable and dashed lines 
indicating direct effects. Statistical significance levels are 
clearly marked alongside each path coefficient (β). 

 
Figure 2. Path analysis of digital marketing dimensions, 
organizational innovation climate, and product innovation 
performance (Note: *p < 0.05; **p < 0.01; ***p < 0.001; ns = not 
significant; Solid lines represent indirect effects; dashed lines 
represent direct effects) 

The model exhibited good explanatory power, with R² 

values of 0.486 for Organizational Innovation Climate (OIC) 
and 0.532 for Product Innovation Performance (PIP), 
indicating that 48.6% of the variance in OIC and 53.2% of the 
variance in PIP were explained by the model constructs. All 
hypothesized pathways were statistically significant except 
for the direct effect of AI Marketing on Product Innovation 
Performance. 

The path coefficient analysis revealed that all three 
dimensions of digital marketing strategies significantly 
influenced organizational innovation climate, with AI 
Marketing showing the strongest effect (β = 0.323, p < 

0.001), followed by Video Marketing (β = 0.289, p < 0.001) 

and Social Media Marketing (β = 0.247, p < 0.01). 
Organizational innovation climate, in turn, had a substantial 
positive effect on product innovation performance (β = 
0.683, p < 0.001). 

Table 6 presents the detailed results of the hypothesis 
testing, including direct, indirect, and total effects. The 
significance of these effects was determined using the 
bootstrapping procedure with 5,000 resamples. 

The results supported Hypothesis 1, confirming that 
digital marketing strategies positively influence product 
innovation performance (β = 0.470, p < 0.001). Hypothesis 

2a, which proposed that digital marketing strategies 
positively influence organizational innovation climate, was 
supported for all three dimensions: Social Media Marketing (
β = 0.247, p < 0.01), Video Marketing (β = 0.289, p < 0.001), 

and AI Marketing (β = 0.323, p < 0.001). Hypothesis 2b, 
suggesting that organizational innovation climate positively 
influences product innovation performance, was also 
supported (β = 0.683, p < 0.001). 
 

 

 



X. Liang et al. /Future Technology                                                                                         August 2025| Volume 04 | Issue 03 | Pages 138-147 

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Table 6. Hypothesis testing results 

Hypothesis Path 
Direct 
Effect 

Indirect 
Effect 

Total 
Effect 

Result 

H1 
Digital 

Marketing 
→ PIP 

- - 
β = 

0.470*** 
Supported 

H2a 
SMM → 

OIC 
β = 

0.247** 
- 

β = 
0.247** 

Supported 

H2a VM → OIC 
β = 

0.289*** 
- 

β = 
0.289*** 

Supported 

H2a AIM → OIC 
β = 

0.323*** 
- 

β = 
0.323*** 

Supported 

H2b OIC → PIP 
β = 

0.683*** 
- 

β = 
0.683*** 

Supported 

H2 
(SMM) 

SMM → 
OIC → PIP 

β = 
0.124* 

β = 
0.169** 

β = 
0.293*** 

Supported 

H2 (VM) 
VM → OIC 

→ PIP 
β = 

0.168* 
β = 

0.197*** 
β = 

0.365*** 
Supported 

H2 (AIM) 
AIM → OIC 

→ PIP 

β = 
0.095 
(ns) 

β = 
0.221*** 

β = 
0.316*** 

Partially 
Supported 

H3 
Differential 

Effects 
- - 

SMM < 
VM < 
AIM 

Supported 

Note: SMM = Social Media Marketing; VM = Video Marketing; AIM = 
AI Marketing; OIC = Organizational Innovation Climate; PIP = Product 
Innovation Performance. *p < 0.05; **p < 0.01; ***p < 0.001; ns = not 
significant. 

 
The mediation analysis provided support for Hypothesis 

2, confirming that organizational innovation climate mediates 
the relationship between digital marketing strategies and 
product innovation performance. For Social Media Marketing, 
the indirect effect through organizational innovation climate 
was significant (β = 0.169, p < 0.01), and the direct effect was 

also significant but weaker (β = 0.124, p < 0.05), indicating 
partial mediation. Similarly, for Video Marketing, both the 
indirect effect (β = 0.197, p < 0.001) and direct effect (β = 
0.168, p < 0.05) were significant, suggesting partial 
mediation. For AI Marketing, the indirect effect was 
significant (β = 0.221, p < 0.001), but the direct effect was 

non-significant (β = 0.095, p > 0.05), indicating full 
mediation. For AI Marketing, the indirect effect was 
significant (β = 0.221, p < 0.001), but the direct effect was 

non-significant (β = 0.095, p > 0.05), indicating full 
mediation. This finding suggests that AI marketing influences 
product innovation performance entirely through its impact 
on organizational innovation climate, highlighting the critical 
importance of establishing supportive organizational 
conditions for AI marketing initiatives to translate into 
innovation outcomes. 

Hypothesis 3, which proposed differential effects of the 
three dimensions of digital marketing on product innovation 
performance through organizational innovation climate, was 
supported. AI Marketing exhibited the strongest total effect (
β = 0.316), followed by Video Marketing (β = 0.365) and 

Social Media Marketing (β = 0.293), highlighting the varying 

impact of different digital marketing dimensions on 
innovation outcomes in educational institutions. 

The model's predictive relevance was assessed using the 
Stone-Geisser Q² value obtained through the blindfolding 

procedure. The Q² values for organizational innovation 
climate (0.283) and product innovation performance (0.324) 
were both greater than zero, indicating that the model had 
adequate predictive relevance. Furthermore, the effect sizes 
(f²) were calculated to assess the magnitude of each 

predictor's effect. The results showed that organizational 
innovation climate had a significant effect on product 
innovation performance (f² = 0.735), while the digital 
marketing dimensions had medium effects on organizational 
innovation climate, with AI Marketing showing the most 
significant effect (f² = 0.170), followed by Video Marketing (f

² = 0.142) and Social Media Marketing (f² = 0.107). 

5. Discussion 

5.1 Theoretical implications 
The results of this study make several salient 

contributions towards understanding the integration of 
marketing, innovation, and education technology in a digitally 
focused environment. As observed, marketing activities have 
both direct and indirect impacts on the innovation 
performance of a product through the organizational 
innovation climate in the context of education in Malaysia. 
This supports other studies, which indicate that marketing 
plays a role that extends beyond just advertising and provides 
vital intelligence needed during the innovation process and 
societal evaluation of product needs [8]. The pronounced 
mediating impact of the organizational innovation climate 
illustrates the pivotal role organizational elements have in 
transforming outside market data into innovative results. 

The differences noticed in the three areas of digital 
marketing give further information on how different digital 
methods impact innovation performance. AI Marketing had 
the most pronounced impact of all on organizational 
innovation climate (β = 0.323), indicating it can considerably 
reshape organizational norms and encourage innovation. 
This corresponds with new scholarship on transformational 
change in organizations, which argues that the adoption of 
sophisticated technologies prompts paradigm shifts in core 
processes and competencies of the organization [17]. 
Institutions of higher learning that fully utilise AI marketing 
tools seem to be in a better position to foster an innovative 
climate conditions because such tools provide real-time 
insights and automation, which improve organizational 
decision-making. 

Video Marketing came out as the second most significant 
predictor of organizational innovation climate (β = 0.289) 

and had a noticeable positive influence on product innovation 
performance (β = 0.168). This finding supports earlier work 
on teaching design thinking in digital marketing courses [7] 
by showing that educational visual narratives are also capable 
of strengthening institutional innovation. The significant 
direct effect suggests that video content may directly shape 
product innovation by providing vivid demonstrations of 
educational delivery models that inspire new approaches to 
educational product development. 

The full mediation observed in the relationship between 
AI Marketing and product innovation performance offers 
particularly valuable theoretical insights that extend our 
understanding of technology-organization interactions in 
educational contexts. While AI Marketing showed the 
strongest effect on organizational innovation climate, its 
direct effect on innovation performance was non-significant, 
indicating that its influence operates entirely through the 
development of an innovative organizational environment. 
This finding provides compelling evidence that advanced 
digital technologies, particularly AI-driven marketing tools, 
require supportive organizational contexts to effectively 
translate into innovation outcomes, reinforcing the socio-
technical perspective in innovation research. 



X. Liang et al. /Future Technology                                                                                         August 2025| Volume 04 | Issue 03 | Pages 138-147 

145 

 

From a theoretical standpoint, this full mediation effect 
suggests that AI marketing technologies function as 
organizational capability builders rather than direct 
innovation drivers. Unlike traditional marketing approaches 
that may have more immediate and direct impacts on product 
outcomes, AI marketing appears to work through a more 
complex pathway that involves reshaping organizational 
norms, processes, and innovation-supportive behaviors. This 
aligns with organizational learning theory, which posits that 
technological inputs must be absorbed and integrated into 
organizational routines before they can generate innovation 
outcomes. The finding indicates that educational institutions 
cannot simply implement AI marketing tools and expect 
immediate innovation returns; instead, they must 
simultaneously cultivate organizational climates that can 
effectively leverage these technological capabilities. 

The context-dependent nature of technology effects 
aligns with research on manufacturing companies' adaptive 
marketing capabilities [18], where organizational factors 
mediate the impacts of technology on performance outcomes. 
This theoretical insight has important implications for 
understanding digital transformation in educational contexts, 
suggesting that successful AI implementation requires a 
holistic approach that addresses both technological and 
organizational dimensions. 

5.2 Practical implications 
The findings yield substantial practical implications for 

educational institutions seeking to enhance their innovation 
performance. The confirmed positive influence of digital 
marketing on innovation outcomes, both directly and through 
organizational climate, highlights the strategic importance of 
digital marketing integration beyond traditional promotional 
objectives. Educational institutions should view digital 
marketing as a strategic capability that not only enhances 
market visibility but also generates valuable insights for 
product innovation. 

The profound impact of organizational innovation 
climate on product innovation performance (β = 0.683) 

indicates that higher education institutions need to foster a 
culture of experimentation, knowledge dissemination, and 
collaborative troubleshooting. Innovation climate is primarily 
shaped by the organization's leadership's willingness to 
promote risk-taking, make adequate resources available for 
innovation projects, and positively acknowledge inventive 
efforts. This aligns with the findings of Tataryntseva and 
Kryvobok [6], who tracked some trends of digital marketing 
usage that enhance financial performance via the 
improvement of innovation performance. 

The varying impacts of the dimensions of digital 
marketing have critical implications for guiding resource 
allocation strategies, particularly regarding AI marketing 
investments. Given the pronounced impact of AI Marketing on 
organizational innovation climate and its full mediation 
effect, educational institutions should adopt a dual-pronged 
approach when implementing AI marketing initiatives. First, 
institutions should focus on building technical capabilities in 
data analysis, interface customization, and automated 
engagement systems. Second, and equally important, they 
must simultaneously invest in creating accommodating 
organizational climates that support the transformation of AI 
insights into educational product innovations. 

This finding has particularly important implications for 
AI marketing implementation strategies. Educational 
institutions should not expect immediate innovation returns 
from AI marketing investments alone. Instead, they should 

plan for a more comprehensive transformation that includes 
leadership development programs to support innovation, 
cross-functional collaboration initiatives, resource allocation 
for experimental projects, and reward systems that 
encourage creative risk-taking. This balanced investment in 
technological and organizational change demonstrates the 
socio-technical approach essential for successful digital 
transformation initiatives. 

The direct and indirect effects of Video Marketing 
demonstrate its dual functionality as a strategic 
communication tool and a catalyst for innovation. Educational 
institutions can use videos to promote their existing offerings 
and, at the same time, consider how visual narratives can 
revolutionise the development of new educational products. 
This dual function explains why video marketing is 
particularly advantageous for resource-constrained 
institutions trying to maximise return on investment from 
digital marketing strategies.  

The less pronounced impact of Social Media Marketing 
implies it might act as a foundational digital marketing 
capability that scaffolds critical connectivity to a market, yet 
offers less distinctive innovation value when compared to 
more sophisticated alternatives. In any case, the substantial 
mark it leaves on both the organizational climate for 
innovation and the overall innovation performance of 
educational products attests to its significant role as a 
marketing toolkit staple for educational institutions. 

5.3 Limitations and future research directions 
This study presents several vital contributions alongside 

inherent limitations that open avenues for future research. 
The cross-sectional design limits causal inferences, as it does 
not allow for a definitive explanation of causation. With 
regard to the marketing strategies, organizational innovation 
climate, and product innovation performance, longitudinal 
studies would be able to account for temporal dynamics to a 
greater extent. Furthermore, the Malaysian context is helpful 
for understanding emerging market dynamics, but also serves 
to limit the scope with regard to generalisability to other 
national educational systems with different technological 
frameworks and cultural approaches towards innovation. 

Future research could explore the temporal dimensions 
of AI marketing implementation and its evolving impact on 
organizational innovation climate over time. Longitudinal 
studies would be particularly valuable in understanding how 
the full mediation effect of AI marketing develops and 
whether direct effects emerge as organizations mature in 
their AI capabilities. This study's findings, combined with Li 
et al. [19] research regarding consumer behaviour 
surrounding the purchase of innovative products, suggest 
that additional research could be conducted to examine how 
these components act as moderators in the relationship 
between digital marketing strategies and innovation in 
educational products. Kauffeld et al. [20] used 
multidimensional frameworks to examine the dimensions of 
digital marketing within educational contexts, providing 
opportunities for further examination of newly emerging 
innovative approaches. 

The use of self-reported measures is another limitation 
because different respondents might have different 
interpretations of innovation performance. Objective 
measures of innovation outcomes, for example, the success 
rate of new programmes launched or their adoption rates, 
could be more reliable. This study concentrated primarily on 
internal organizational factors, with limited attention to the 
external environment. Future research could analyse the 



X. Liang et al. /Future Technology                                                                                         August 2025| Volume 04 | Issue 03 | Pages 138-147 

146 

 

impact of competitive intensity, regulatory environment, or 
technological turbulence as moderators of the relationships 
examined in this study. 

Research might also focus on the microfoundations of 
innovation climate in educational institutions, examining how 
organizational leadership styles, structural configurations, 
and human resource policies shape supportive environments 
for innovation. Furthermore, comparative studies across 
different types of educational institutions, such as public 
versus private or traditional versus online institutions, could 
reveal context-specific patterns regarding the effectiveness of 
digital marketing strategies for innovation outcomes. 

6. Conclusion  

This research examined the interactions between digital 
marketing, organizational innovation climate, and product 
innovation performance in educational institutions in 
Malaysia. The results indicated that the constituent elements 
of digital marketing, namely, social media marketing, video 
marketing, and AI marketing, impact product innovation 
performance both directly and indirectly through 
organizational innovation climate. AI marketing was the most 
dominant factor impacting organizational innovation climate, 
with video marketing and social media marketing following 
sequentially. These findings illustrate that not all dimensions 
of digital marketing have the same influence. A considerable 
portion of the impact of organizational innovation climate on 
productivity underscores the need to foster an environment 
that encourages innovation in order to fully leverage 
investments in marketing technologies. Educational 
institutions that want to strengthen their innovative 
capabilities should adopt sophisticated digital marketing 
models while simultaneously fostering climates that support 
organizational innovation to promote versatility in 
innovation tools and techniques at their disposal. Such an 
approach allows educational institutions to better utilise the 
insights garnered through digital marketing to develop 
innovative educational products that respond to the changing 
needs of the students and the marketplace. With ongoing 
developments in technology and shifting student 
expectations, embedding digital marketing within innovation 
processes represents one of the most strategic pathways for 
educational institutions to preserve their competitive edge 
and stay relevant. The results of this research add to the 
knowledge base of theory and provide actionable insight on 
how digital marketing can augment innovation performance 
in education, especially in newer educational markets dealing 
with the challenges of digital transformation. 

Acknowledgements 
The authors acknowledge the UKM-GSB, grant number GSB-
2025-007, funded by the Graduate School of Business, UKM. 

Ethical issue 
This research received institutional review board approval, 
and all participants provided informed consent prior to data 
collection. The authors are aware of and comply with best 
practices in publication ethics, specifically with regard to 
authorship (avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The author adheres to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

 
 
 

Data availability statement 
The survey data supporting the conclusions of this article are 
available from the corresponding author upon reasonable 
request while maintaining participant confidentiality. 

Conflict of interest 

The authors declare no potential conflict of interest. 

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