|
INTERNATIONAL JOURNAL OF SOCIAL SERVICE AND
RESEARCH |
THE INFLUENCE OF KNOWLEDGE CREATION AND KNOWLEDGE SHARING
ON PRODUCT DEVELOPMENT WITH PRODUCT INNOVATION MEDIATION EMPIRICAL STUDY ON THE
R&D FOOD INDUSTRY
Fedora Sanchia Tiyana*, Budi Susanto, P.M. Winarno
Faculty of Business, Universitas Multimedia
Nusantara, Tangerang, Indonesia
Email: fedora.sanchia@[email protected]*
Abstract
In this study, the effect of knowledge
creation and knowledge sharing on product development through the mediation of
product innovation was investigated. This research was conducted because of the
research gap in previous studies regarding the effect of knowledge creation and
knowledge sharing on product development. This research is quantitative and was
conducted by distributing questionnaires to food and beverage companies in
Jakarta and obtaining 50 respondents. The data obtained were processed using
SMART-PLS. According to the findings of this study, knowledge sharing has no
significant effect on product development; knowledge creation has no
significant effect on product development; knowledge creation has a significant
positive effect on product innovation; knowledge sharing has a significant
positive effect on product innovation; product innovation has a significant
positive effect on product development; product innovation mediates the
relationship between knowledge creation and product development, and product
innovation mediates the relationship between knowledge sharing and product
development. From the result, it can be suggested that the company needs to
design activities, create internal company forums, create a database for the
R&D department, and require employees to carry out activities outside the
company. Both suggestions can be adapted to the majority of the working
executives' generation. It is expected to be more effective in implementation.
Keywords: Food industry; knowledge
creation; knowledge sharing; product development; product innovation
Received 1
October 2022, Revised 14 October 2022, Accepted 24 October 2022
INTRODUCTION
The food industry contributes to national economic
expansion. This is reinforced by the Ministry of Industry which projects the
food and beverage industry to grow above 5 percent throughout 2021 (Lestari, 2021). To support it,
the government provides intensive import duties borne by the government for the
import of several raw materials.
The food industry in Indonesia, especially during the
pandemic, is experiencing a downward trend (Figure 1). The decline in this
trend can be caused by several things, one of which is the lack of product
innovation carried out by food industry companies during the pandemic. After
the pandemic, people's consumption patterns have changed, so the food industry
is required to be more active in innovating (Kementerian Perindustrian Republik Indonesia, 2021).
Figure
1. Annual Growth of The Indonesian Food Industry
Source: DataIndustri.com
Companies with a competitive advantage in this market are
frequently those with distinctive products, procedures, and customer services (Hidayat et al., 2021). One of the ways
to increase the competitive advantage is to carry out product innovation which
means launching new products according to market needs.
Innovation is one of the most important components
underlying a company's long-term competitive advantage (Cheng & Nasurdin, 2010). Also, product
development is necessary because of the nature of consumers who tend to get
bored easily. There are three types of development in developing food products,
namely: making new products, modifying existing products, and imitating other
products (Ilmayana, 2021). In conducting
product innovation, the food industry is currently investing in its internal
resources, such as the research and development (R&D) team. In today's
quickly changing business world, a strong R&D operation is seen as a
fundamental enabler of competitive advantage (Cho, 2018). To be
a success in developing the product, a good knowledge resource is needed.
Knowledge and technology are becoming more widely
recognized as strategic assets and main sources of competitive advantage (Lai
& Lin, 2012). Therefore, currently, several companies are
working hard to implement knowledge management. Knowledge management is a
management function that involves the generation of knowledge, the management
of knowledge flow throughout the organization, and the effective and efficient application
of knowledge for the long-term benefit of the business (Cheng & Nasurdin, 2010). The improvement
in organizational performance will be achieved, when the management of
knowledge is done properly (Victoria et al., 2020).
Currently, the
food industry is trying to implement knowledge management to improve
organization performance through product development. Applying knowledge
management in companies aims to have systematic data, thus enabling companies
to make better decisions. To be useful for the organization, the knowledge
management in the organization must run well. Knowledge management includes its
components, namely knowledge creation, knowledge sharing, and knowledge
application.
However, in research on knowledge management, product
innovation, and product development, there is a research gap. Research (Rajapathirana & Hui, 2018)
entitled The Relationship Between Knowledge Management and Innovation Performance
demonstrates that knowledge management activities have direct and indirect
effects on innovation and organizational performance via a rise in innovation
capability. Additionally, it was discovered that knowledge creation, knowledge
integration, and knowledge application enhance innovation and performance. This
contrasts with the research conducted by (Victoria et al., 2020). In their article
titled Knowledge Management and Performance of Organizations: A Case Study of
Selected Food and Beverage Firms, they demonstrate that knowledge creation has
a significant negative impact on innovation, whereas knowledge sharing has a
significant positive impact on innovation.
The differences in the results of these studies are also
seen in the current food industry R&D. In the data obtained from interviews
with 7 respondents from 7 different companies. It shows that
all respondents have been active in sharing knowledge in the R&D
department, by holding sharing sessions regarding project progress in each
R&D team. In the sharing session, each individual R&D shared about the
progress of the project being carried out, along with the problems they face in
developing new products. However, the majority of respondents have not
implemented knowledge creation in the R&D department. This can be seen from
the majority of respondents who do not store research results in the company
system. In the absence of data stored in the company's system (for products
that have not been launched), it will hinder the exchange of information in the
event of an employee exchange, because the research data is only known by
researchers. In addition, the R&D of each company issues a different number
of new products each year. This identifies that there are obstacles in product
development.
Based
on field observations, not all products developed by R&D will be launched
in the market. One of the factors is the changing market trends. This delay in
launch time can be caused by the length of time required for product
development, which is due to the lack of information obtained about market
trends.
Because
knowledge is one of the most essential aspects in the formation of a new
product or process concept, the organization must manage knowledge creation for
the process of developing new products or processes to continue (Indriartiningtias
et al., 2017).
Knowledge is created by each individual and the organization creates an
environment that can encourage individuals to be creative and to produce new
knowledge. Therefore, organizations need to provide a good and structured
platform for individuals within the company, so that the company and others can
use the knowledge.
The study “Knowledge Management and Performance of
Organizations: A Case Study of Selected Food and Beverage Firms” (Victoria et al., 2020) demonstrates that knowledge creation has a significant
negative effect on innovation. It also discovers that knowledge sharing has a
significant positive impact on innovation. Knowledge creation has a significant
positive effect on job satisfaction, whereas knowledge sharing has an
insignificant negative effect.
Moreover, Bandinelli
et al. (2014)
found that the
correlation between Knowledge Management maturity and New Product Development
in the Electrical industry is positive.
In addition, Daschievici
and Ghelase (2014)
examine that knowledge
management model for food research, supported by cutting-edge information
technology.
Based
on those phenomena, this study aims to examine
•
Knowing the effect of knowledge sharing on product development
•
Knowing the effect of knowledge creation on product development
•
Knowing the effect of knowledge creation on product innovation
•
Knowing the effect of knowledge sharing on product innovation
•
Knowing the effect of product innovation on product development
•
Knowing the effect of product innovation as a mediator on the
relationship between knowledge creation and product development
•
Knowing the effect of product innovation as a mediator on the
relationship between knowledge sharing and product development.
Research Hypothesis
Knowledge
sharing refers to the communication between team members that are required for
product development (Cheng & Nasurdin, 2010b). Project success is based on effective knowledge sharing
in complex, time-consuming interactions (Thamhain, 2004). When organizations are effective at sharing knowledge,
the flow of information is increased, allowing the organization to generate
superior products. Based on observations, it is suspected that knowledge
sharing affects product development because, with the exchange of knowledge
between individuals, individuals will acquire new knowledge and can apply this
knowledge to product development. Based on support from the literature, The following
hypothesis is:
H1: Knowledge sharing has a positive effect on product
development
Working on product development allows for team-based
knowledge creation, problem-solving, and brainstorming to address
product-related challenges (Poh
Kiat Ng et al., 2011). The
study discovered a relationship between new product development and the
generation and management of new knowledge (Cheng
& Nasurdin, 2010b). With the creation of good
knowledge within the company, the R&D team will be able to easily obtain
the knowledge needed to support product development. Therefore, following
these previous studies, the hypothesis is:
H2:
Knowledge creation has a positive effect on product development
Based on research
Victoria et al. (2020), The creation of knowledge had a significant negative
impact on innovation. However, the
study by Rajapathirana and
Hui (2018) shows that
knowledge creation facilitates innovation.
Moderating effects of knowledge acquisition improve new product performance (Rajapathirana & Hui, 2018). Based on previous research, it is said that Knowledge
creation, integration, and application enhance innovation and performance.
Also, it is said that The effectiveness of knowledge acquisition has a
significant positive relationship with product innovation. Based on the
literature, the next hypothesis is:
H3: Knowledge creation has a positive effect on product
innovation
According to research
Victoria et al. (2020), knowledge sharing had a significant positive effect on
innovation. "Innovative information use," "efficient information
collection," and "shared interpretation" are the knowledge
management tools. increase the performance of new items and the ability to
innovate (Rajapathirana & Hui, 2018). Based on the research that has been done, it is known
that knowledge sharing has a significant positive effect on innovation. Based
on it, the hypothesis used in this study is:
H4:
Knowledge sharing has a positive effect on product innovation
Process
and product innovation are both examples of innovation. Process innovation does
not deliver a new product to the market. It helps to provide a better solution
to meet existing or new requirements. Product development also includes new
product development and existing product development. Existing product
development requires minimal innovative thinking as no novel ingredients or
processes are required.
Product
innovation is the management framework for making adjustments and enhancements
to products (Rainey, 2009). It entails the development, validation, and marketing of
new products, as well as their conceptualization, design, and development.
Thus, new product development becomes part of product innovation. Successful
new products are developed through an effective new product development process
that streamlines the flow of activities and outcomes from concept to commercialization
by combining previous new product development program knowledge with the skills
and abilities of the participants (Rainey, 2009).
H5: product
innovation has a positive effect on product development
H6: product innovation mediates the relationship between
knowledge creation and product development
H7: product innovation mediates the relationship
between knowledge sharing and product
development
METHOD
This quantitative study’s subjects are
Indonesian food industry research and development executives in Jakarta.
Meanwhile, the sample for this study consisted of 50 food industry research and
development executives in Jakarta. This study was preceded by
conducting interviews with 7 respondents according to the
research criteria. The
results of these interviews are then used as evidence data in this study. After
obtaining evidence data, compiling the theory of previous research, and
compiling research hypotheses, then proceed with compiling a questionnaire.
The non-probability sampling technique was
used in conjuction with a judgmental sampling
technique. This questionnaire is then distributed online.
After obtaining 30 respondents
who met the criteria, a pre-test was carried out. The
purpose of this pre-test is to find out whether the items in the questionnaire
are valid. After the pre-test data were processed using SPSS and were valid,
the questionnaire was distributed and continued. After 50 respondents was
collected, several tests were carried out, namely instrument testing,
descriptive analysis, and hypothesis testing. Insturment test
and hypothesis test are carried out by SMART-PLS, using Structural Equation
Modeling (SEM) with 10% significance level.
RESULTS AND DISCUSSION
Several
tests, including descriptive statistics, validity tests, reliability tests, and
hypothesis testing, were performed in this study. Descriptive statistics were
processed using SPSS. While the other tests were processed using SMART-PLS. The
results of each test are described in Figure 2 and will be explained below.
A validity test was
carried out using SMART-PLS. The validity test analysis uses two methods,
namely discriminant validity and construct validity.
The degree to which a construct differs from others is
referred to as discriminant validity (Acosta-Prado et al., 2020).
The square root of each
construct's AVE must be greater than the correlation with any other construct,
according to Fornell- Larcker's
criterion (Acosta-Prado et al., 2020). Based on the test
findings and this theory (Figure 5), all
variables are valid.
In
addition to looking at the Fornell-Larcker value, the cross-loadings value is
also seen. The correlation of specific items with all constructs inside the
model, including the construct they are meant to reflect, is measured by cross-loading
(Achjari, 2004).
The criterion is that an item's load should be higher for the construct it is
required to reflect than for the other constructs (Achjari, 2004). Based on the
results obtained (Figure 6), it can be said that all constructs are following
the theory.
To see the reliability of
an indicator, initially, an analysis of the value of the outer loadings of each
indicator is carried out. Individual item reliability is tested using outer loadings, with suitable values given to those
over 0.708 (Achjari, 2004). Based on this
theory, the following are indicators that have a value ≥ 0.708.
Malhotra et al. (2007) said that the reliability
coefficient is between 0.70-0.90. And based on (Acosta-Prado et al., 2020) it
is said that the value of AVE is said well if greater than 0.500, which means
it accounts for more than 50% of the variation in the items it reflects. Figure
8 demonstrates that each variable's Cronbach's Alpha is greater than 0.70 and
the AVE is greater than 0.50, which means that all variables in this study are
reliable.
In this study, 50
respondents met the criteria, namely respondents who worked as R&D for the
food industry in Jabodetabek. Based on the data obtained from the respondents,
then a descriptive analysis was carried out, to determine their
characteristics. The following are the gender, age, and length of work, of the
respondents.
Based on data obtained
from 50 respondents who met the criteria, there were 78% female respondents,
and 22% male respondents.
Figure 7.
Respondents' profiles Based on Gender
Based on the data,
respondents are dominated by the age group of 26-30 years, which is 62% of the
total respondents. Meanwhile, the second largest respondent was in the age
group of 21-25 years, which was 22%. And the least respondents are respondents
with the age group > 35 years, which is 4% of the total respondents.
Figure 8. Respondents'
Profile Based on Age
Based on the results
obtained, there are 58% of the total respondents who work as R&D in the
food industry for 1 – 5 years. Meanwhile, respondents who worked in R&D for
6 - 10 years were 42%.
Figure 9. Respondents' Profile Based on Length
of Working as R&D
In the questionnaire, a
Likert scale was used which was divided into 5 categories in the respondents'
answers, namely strongly disagree (1), disagree (3), neutral (3), agree (4),
and strongly agree (5). Therefore, the interval used is 0.8. The value of the
interval is obtained by subtracting the maximum value (5) from the minimum
value (1) and dividing it by the total number of answers (5). Table 1 shows the categories of these interval.
Table 1
Interval Category
|
Interval |
Category |
|
4.20 < a ≤ 5.00 |
Strongly Agree |
|
3.40 < a ≤ 4.20 |
Agree |
|
2.60 < a ≤ 3.40 |
Neutral |
|
1.80 < a ≤ 2.60 |
Disagree |
|
1.00 < a ≤ 1.80 |
Strongly Disagree |
Based on the
predetermined interval, then the calculation of descriptive statistics using
SPSS is carried out. From 50 respondents who filled out the questionnaire,
descriptive statistics were obtained, such as the frequency of the answer
scores, means, and category of each indicator. Tables below display descriptive
statistics.
Table 2
Knowledge Sharing Mean Score
|
No |
Indicators |
Response Score Frequency |
Mean |
Category |
||||
|
|
Knowledge Sharing |
1 |
2 |
3 |
4 |
5 |
||
|
1 |
Ability to solve
problems with creative solutions |
0 |
0 |
4 |
25 |
21 |
4.34 |
Strongly Agree |
|
2 |
Ability to convey
knowledge gained from internal and external companies |
0 |
1 |
8 |
21 |
20 |
4.20 |
Agree |
|
3 |
Ability to socialize
and communicate with others. |
0 |
0 |
7 |
23 |
20 |
4.26 |
Strongly Agree |
|
|
|
Knowledge Sharing Mean
Score |
4.27 |
Strongly Agree |
||||
As shown
in Table 2, respondents strongly agree with sharing of knowledge. It showed from the
total score of 4.27 on this variable. The most significant indicator is the
ability to solve problems with creative solutions. It is indicated by a score
of 4.34. It shows that respondents, members of the R&D team, actively share
knowledge, so that they can solve problems with creative solutions.
Table 3
Knowledge Creation Mean
Score
|
No |
Indikator |
Response Score Frequency |
Mean |
Category |
||||
|
Knowledge Creation |
1 |
2 |
3 |
4 |
5 |
|||
|
1 |
Process |
0 |
0 |
9 |
25 |
16 |
4.14 |
Agree |
|
2 |
Output |
0 |
0 |
9 |
26 |
15 |
4.12 |
Agree |
|
3 |
The organization facilitates space for create knowledge |
0 |
3 |
4 |
24 |
19 |
4.18 |
Agree |
|
|
|
Knowledge Creation Mean
Score |
4.15 |
Agree |
||||
In Table 3, the total score of knowledge creation is 4.15,
which means that the respondents agree with the variable. The three indicators
on the knowledge creation have scores that are not significantly different,
namely 4.14 for the process, 4.12 for the output, and 4.18 for the organization
facilitates space for creating knowledge. It shows that during team meetings,
respondents provide information to each other about the stages of product
development that are being carried out and show ideas obtained from the
knowledge creation process. The company where the respondents work also
facilitates the creation of knowledge within the company.
Table 4
Product Innovation Mean Score
|
No |
Indikator |
Response Score Frequency |
Mean |
Category |
||||
|
Product Innovation |
1 |
2 |
3 |
4 |
5 |
|||
|
1 |
Uniqueness |
1 |
3 |
4 |
29 |
13 |
4.00 |
Agree |
|
2 |
Quality |
0 |
1 |
5 |
23 |
21 |
4.28 |
Strongly Agree |
|
3 |
Multifunction |
0 |
6 |
13 |
22 |
9 |
3.68 |
Agree |
|
4 |
Research |
0 |
3 |
8 |
20 |
19 |
4.10 |
Agree |
|
|
|
Product Innovation Mean Score |
4.015 |
Agree |
||||
Table 4 shows the product innovation
mean score, it shows that
the most significant indicator is quality. It is indicated by a
score of 4.28. It means that the respondent regularly generates product ideas with
the best quality to support
product innovation.
Table 5
Product Development Mean Score
|
No |
Indikator |
Response Score
Frequency |
Mean |
Category |
||||
|
Product Development |
1 |
2 |
3 |
4 |
5 |
|||
|
1 |
Efficiency |
1 |
5 |
4 |
24 |
16 |
3.98 |
Agree |
|
2 |
Market oriented |
0 |
2 |
6 |
20 |
22 |
4.24 |
Strongly Agree |
|
3 |
Product improvement |
0 |
1 |
6 |
22 |
21 |
4.26 |
Strongly Agree |
|
|
|
Product Development Mean Score |
4.16 |
Agree |
||||
For product development, respondents most agree with
market-oriented and product improvement indicators. It is indicated by scores
of 4.24 and 4.26. The score indicates that the respondent is developing new
products according to market needs and developing new products with improved
performance compared to previous products.
Figure 10 demonstrates the
value of the R square. R square value for product innovation is 0.571. This
means that the independent variables (knowledge sharing and knowledge creation)
affect product innovation by 0.571 or 57.1%. This value indicates that the
independent variable has a moderate effect on product innovation.
Figure 10. R-Value
For the product
development variable, the R square value obtained is 0.551. This proves that
knowledge sharing, knowledge creation, and product innovation affect product
development by 55.1%. This value indicates that these three variables have a
moderate effect on product development.
The following research
model is derived from the final results of the main test with 50 samples that
meet the respondents' criteria. The path diagram of the SMART-PLS software
shows how much the independent variable influence the dependent variable.
Figure 11. Hypothesis Test
The hypothesis is
accepted if the two-tailed t-statistic is greater than 1.65 and the p-value is
less than 0.10. Figure 12 demonstrates the outcomes of the hypothesis test. This
table indicates that the p-value of knowledge sharing on product development
(H1) and knowledge creation on product development (H2) is greater than 0.10.
This shows that the two hypotheses are not accepted.
In
Figure 13, the results of hypothesis 6 (Product
innovation mediates the relationship between knowledge creation and product
development) and hypothesis 7 (Product innovation mediates the relationship
between knowledge sharing and product development) test are shown. The results
of the H6 and H7 tests showed that the p-values for H6 were 0.093 and 0.020 for
H7. This means that both hypotheses are accepted.
Table 6 shows the outcomes of
the hypothesis testing. The table shows the t-value and p-value of each
hypothesis. Based on this value, it can be concluded that the variables are
accepted and not accepted.
Table 6
Hypothesis Test
|
Research Hypothesis |
t-value |
p-value |
Conclusion |
|
H1: Knowledge sharing has a positive effect on product development |
0.144 |
0.885 |
Not Accepted |
|
H2: Knowledge creation has a positive effect on product development |
1.112 |
0.267 |
Not Accepted |
|
H3: Knowledge creation has a positive effect on product innovation |
2.240 |
0.026 |
Accepted |
|
H4: Knowledge sharing has a positive effect on product innovation |
2.481 |
0.013 |
Accepted |
|
H5: Product innovation has a positive effect on product development |
3.850 |
0.000 |
Accepted |
|
H6: Product innovation mediates the relationship between knowledge creation and product development |
1.683 |
0.093 |
Accepted |
|
H7: Product innovation mediates the relationship between knowledge sharing and product development |
2.334 |
0.020 |
Accepted |
Table 6 shows that the t-value and p-value for H1 are 0.144 and 0.885,
respectively. The p-value is greater than 0.10, and the t-value is less than
1.65, indicating that knowledge sharing has no significant direct effect on
product development.
The results in Table 6 show that the t-value and p-value for H2 are 1.112
and 0.267, which means the t-value is < 1.65 and the p-value is > 0.10.
These findings indicate that H2 cannot be supported. This means that knowledge
creation has no significant effect on product development.
The t-value and p-value
for H3 are 2.240 and 0.026, respectively, as shown in Table 6. This value indicates that H3 is accepted because
the t-value is greater than 1.65 and the p-value is less than 0.10. These
results mean that knowledge creation has a significant effect on product
innovation.
Knowledge creation facilitates innovation (Rajapathirana & Hui, 2018).
Knowledge is seen as the key to innovation and a valuable commodity for
businesses looking to obtain a competitive advantage over their competitors.
Successful companies can create and disseminate knowledge rapidly, then
transfer the knowledge into new products (Gao & Bernard, 2018).
Knowledge is tacit, scattered, and ingrained within
individuals (Park et al., 2015). As a result, the
organization must devise initiatives to elicit knowledge from its employees,
stakeholders, suppliers, and other third parties. These activities should
involve all aspects of individuals, both inside and outside the company, who
can provide new knowledge to company members.
By creating good knowledge in the company, employees will
easily gain new knowledge. This knowledge usually covers the latest market and
technology trends. This is what can later be applied in product innovation so
that the new product will be better than the previous product.
Based on the results
obtained, the t-value and p-value for H4 are 2.481 and 0.013, which indicates
that the t-value is >1.65 and the p-value is <0.10. These results
indicate that H4 is approved, which means that knowledge sharing has a
significant effect on product innovation.
The sources of new knowledge are tacit knowledge and
lived experience. It can be found in personal knowledge and experiences (Park et al., 2015). It enabled
effective knowledge sharing in the product discovery process and the attainment
of competitive advantage. Effective knowledge exchange accelerates and enhances
product innovation (Gao & Bernard, 2018). Knowledge sharing
allows people to better their work performance and generate new ideas and
innovations (Gao & Bernard, 2018).
By actively sharing knowledge within the R&D team,
each individual in the team will acquire new knowledge regularly. This knowledge
will help the team in product innovation. In addition, having new knowledge
gained from knowledge sharing will help employees in solving problems they
encounter with creative solutions.
Table 6 reveals that the t-value for H5 is 3.850, which
means it is greater than 1.65. Meanwhile, the p-value is 0.000, which indicates
that it is less than 0.10. These data indicate that hypothesis H5 is accepted,
in which product innovation has a significant effect on product development.
Product innovation is the organizational framework for
introducing product enhancements (Rainey, 2009). It encompasses
the conception, design, development, validation, and marketing of new products.
Thus, product innovation includes new product development. And vice versa.
Product innovation is part of product development because innovation is closely
related to applying new ideas to the product, which is the same as developing
new products.
Table 6 shows that the t-value and p-value for H6 are 1.683 and 0.093. This shows
that H5 is accepted because the t-value is > 1.65 and p-value < 0.10. These
results mean that knowledge creation has a significant effect on product
development through product innovation mediation. In other words, knowledge
creation has an indirect effect on product development.
From the results
obtained, it is known that the t-value and p-value for H7 are 2.334 and 0.020.
Based on these results, It signifies that H7 is accepted and demonstrates that
knowledge sharing has a significant effect on product development via product
innovation mediation.
The findings of Hypotheses 1 and 2 indicate that
knowledge creation and knowledge sharing have no direct impact on product
development. Meanwhile, the results of H6 and H7 show that the relationship
between knowledge creation and product development, as well as the relationship
between knowledge sharing and product development, are mediated by product
innovation. The results of H1 and H2 demonstrate that the two independent variables
have no direct effect on product development, which may be due to the presence
of a mediator in the relationship. And this is shown by the results of H6 and
H7 which show that product innovation is a mediator between knowledge creation
and knowledge sharing with product development.
In developing a product, food and beverage R&D will
add or substitute ingredients or processes to the product. The replacement or
addition of materials or processes is aimed at improving product quality,
making processes more efficient, or developing product variants. Generally,
this is done in the development of existing products.
Meanwhile, R&D is also developing completely new
products. This product development usually combines market trends and the
latest technology, resulting in products that have never been on the market
before. Both of these product developments, apply the concept of innovation in
practice. Thus, product development is closely related to product innovation.
In other words, you can't develop a product without innovation in it.
The knowledge obtained by R&D from the results of
knowledge creation and knowledge sharing, if applied to product development,
which means implementing new ideas or processes in it, means doing product
innovation. Therefore, product innovation becomes a mediator between knowledge
creation and knowledge sharing with product development.
Product innovation is crucial in product development. By
innovating in product development, the product has added value that competitors
do not have. By doing product development, R&D is simultaneously
innovating. In other words, an R&D cannot develop a product without
implementing innovation in it.
CONCLUSION
Based on research problem, objective, and data processing
that has been done by distributing online questionnaires, obtained 50
respondents from R&D executives of food and beverage companies in Jakarta.
From the data obtained, it is then processed using SMART-PLS. Validity test,
reliability test, descriptive test, and hypothesis test are conducted.
Hypothesis testing was conducted with a significance level of 10%. The outcomes of hypothesis
testing are : 1) knowledge sharing has no significant effect on product development,
2) knowledge creation has no significant product development,
3) knowledge creation has a positive effect on product innovation,
4) knowledge sharing has a positive effect on product innovation,
5) product innovation has a positive effect on product development,
6) knowledge creation and
product development are mediated by product
innovation, and 7) the relationship between knowledge sharing and product
development is mediated by product
innovation
Based on it, can be concluded that knowledge creation and
sharing have no direct influence on product development. Knowledge creation and knowledge sharing
affect product development, mediated by product innovation.
In this study, the scope of research, independent variables, and indicators
used are limited. In further research, researchers can expand the research
area, so that the data obtained are more varied and the results obtained can
describe the situation in a wider scope. Also, the researcher can add research
variables and their indicators.
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