Next Article in Journal
Possibilities, Challenges, and Future Opportunities of Microgrids: A Review
Previous Article in Journal
International Joint Ventures’ Knowledge Acquisition: Critical Literature Review
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 15
Issue 8
10.3390/su15086365
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Factors That Boost the Technological Capability of Malaysian Food Manufacturing Industry

by
Hashem Salarzadeh Jenatabadi
,
Che Wan Jasimah Wan Mohamed Radzi
*,
Nursyakirah AbdManap
and
Nor Aishah Abdullah
Department of Science and Technology Studies, Faculty of Science, Universiti Malaya, Kuala Lumpur 50603, Malaysia
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(8), 6365; https://doi.org/10.3390/su15086365
Submission received: 7 February 2023 / Revised: 11 March 2023 / Accepted: 21 March 2023 / Published: 7 April 2023
Browse Figures
Versions Notes

Abstract

:
The increasing urbanization of the world has created new issues and challenges for the modern food industry to meet customers’ changing demands. To address these issues, these firms need to invest in technological capability development. This study examines factors that affect the technological capability of food manufacturing firms. A large-scale survey of a sample of 270 food manufacturing firms in Malaysia was conducted. Structural equation modeling was used to evaluate the observed variables. The result shows that organizational innovation fully mediates the relationship between organizational learning and technological capability. Overall, the findings suggest that food manufacturing firms in Malaysia that want to enhance their technological capability should focus on developing and implementing organizational innovation practices, in addition to other strategies and practices that aim to promote organizational learning and innovation. This research contributes to the development of related theories and has practical implications for the stakeholders in the food manufacturing industry.

1. Introduction

The increasing urbanization of the world has created new challenges for the modern food industry as consumers demand higher quality, safety, and variety in their food products. To meet these demands, the manufacturing of food has become more diverse and complex, requiring the adoption of new innovative processing technologies. There has been a focus on improving the efficiency, safety, and stability of food products in a healthier way [1].
In Malaysia, the food manufacturing sector is one of the key industries that contribute to economic and social growth. Malaysia managed to double its export value over the last ten years. However, Malaysia heavily relies on imported food products as the local production is insufficient to cater to the country’s ever-growing population. Malaysia is a net importer of food, with processed food imports totaling MYR 20 billion [2] and about 24% of the total food supply in the country is imported [3]. Therefore, this is an issue of grave concern as it affects food security and sustainability, as evidenced by what happened in Singapore when the COVID-19 outbreak hit. Singapore imports nearly 90% of the food it consumes locally, and as the 2020 global lockdown disrupted supply chains, it induced panicked consumer behavior and worsened food insecurity for vulnerable communities as their incomes declined [4]. Malaysia is committed to implementing the 2030 Agenda for Sustainable Development; hence, efforts must be focused on creating a food production system that is sustainable, enhances productivity, and increases output, all while ensuring that food is abundant, safe, reasonably priced, and nutritious.
Food manufacturing firms in Malaysia face a number of challenges, including competition, food safety and quality, sustainability, cost, and market opportunities [5]. To address these issues, it is important for these firms to invest in technological capability development. By implementing technological solutions, a firm can streamline its operations, automate tasks, and improve the accuracy and speed of its processes. This can lead to significant cost savings and increased output, which can translate into increased profitability and competitiveness [6,7]. By adopting these technologies, food manufacturing firms in Malaysia can improve their production processes, increase efficiency, reduce costs, improve the safety and quality of their products, reduce their environmental impacts, and expand their market opportunities [8,9]. Overall, investing in technological capability development can help food manufacturing firms in Malaysia stay competitive, improve their sustainability, and meet the evolving needs of consumers.
Based on Grant’s [10] theory on the firm’s knowledge and Cohen and Levinthal’s [11] theory on absorptive capacity, this study proposed knowledge management and organizational learning as the antecedents of technological capability. Organizational innovation is used as the mediator for both variables.
Previous studies highlighted that a firm’s technological capability developed over time and accumulated through its past experiences [12,13]. Through the knowledge management process, firms that can accumulate their technological knowledge will have the enhanced ability to employ new technologies in their operations and product development processes [14]. In other words, knowledge management helps the firm improve its technological capability.
Ultimately, ineffective knowledge management practices can result in a loss of competitive advantage, decreased efficiency, and reduced innovativeness, all of which can have significant consequences for a firm’s sustainability. Without proper knowledge management, knowledge assets can become obsolete and ineffective, hindering efficient decision making and problem solving. In addition to this, if knowledge is not shared across the firm’s levels, valuable insights and expertise may be lost, resulting in missed opportunities and decreased productivity. Empirical studies have demonstrated the positive impact of knowledge management on firms’ competitive advantage, underscoring the importance of effective knowledge management in contemporary firms [15,16,17].
Moreover, past works of literature also conducted studies that look at organizational learning and technological capability. Organizational learning plays a crucial role in an organization’s ability to develop and enhance its technological capability. Failing to invest in effective learning practices can limit an organization’s ability to enhance its technological capabilities and adapt to dynamic market environments, ultimately hindering growth and competitiveness [18,19]. Therefore, it is essential for organizations to prioritize organizational learning to stay competitive and ensure they can adapt to rapidly changing market environments.
In this study, organizational innovation is used as the mediator in the framework. Mixed research findings suggest that the relationship between organizational learning and technological capability is not straightforward [20,21]. For instance, merely “learning by using” is insufficient to develop technological capability; instead, knowledge flows and organizational learning must be supported through the integration of other aspects [22]. Additionally, the sources of learning, including internal, local, and global sources, are continually evolving as firms’ technological capabilities progress [23].
Despite the wealth of literature in the field, there is a lack of studies that investigate the development of technological capability. Given the rising importance of technology in the food manufacturing industry, there is an essential need to identify the antecedents of the technological capability of the firms. To fill this gap, the current study analyzes the multi-dimensional relationship between knowledge management, organizational learning, organizational innovation, and technological capability and examines how knowledge management and organizational learning processes impact the organizational innovation and technological capability of the firm.

2. Review of the Literature

2.1. Underpinning Theories

The current study is founded on the knowledge-based theory of the firm and absorptive capacity theory to develop a new theoretical framework to investigate the antecedents of technological capability. KBV determines the characteristics of knowledge that have important implications for the competitive advantage of the firm [10]. On the other hand, absorptive capacity has been defined as “the ability of a firm to recognize the value of new external information, assimilate it and apply it for business purposes [11]. Zahra and George [24] further develop our understanding by describing absorptive capacity as a set of organizational routines required to identify and utilize knowledge. These perspectives have a common ground in that they both suggest the enhancement of a firm through knowledge as a sustainable competitive advantage.
According to the KBV theory, a firm’s knowledge base is a critical factor in its competitiveness and success in the market. Knowledge is regarded as a very unique strategic resource that can produce increasing returns and does not degrade over time like traditional economic productive factors do. Knowledge resources are particularly important to ensure that competitive advantages are sustainable, as these resources are difficult to imitate and are the foundation for sustainable differentiation. In this context, knowledge management (KM) is known as a tool to manage knowledge resources [25]. The KBV theory suggests that effective knowledge management is essential for a firm to fully leverage its knowledge base and achieve its strategic goals. Effective knowledge management allows organizations to create new knowledge, acquire knowledge from external sources, and use knowledge to create value, which can improve technological capabilities and increase competitiveness.
On the other hand, absorptive capacity is defined as the firm’s capacity to assimilate and use the knowledge transferred [11]. There can be too little as well as too much technological learning taking place in firms. The absorptive capacity theory explains organizational learning by focusing on an organization’s ability to recognize the value of new knowledge, assimilate it, and apply it to commercial ends. According to this theory, an organization’s absorptive capacity is the key determinant of its ability to learn and absorb new knowledge and technologies. Organizations with high absorptive capacity can effectively recognize, assimilate, and apply new knowledge, while those with low absorptive capacity may struggle to do so.

2.2. Technological Capability

In the literature, technological capability is defined in a variety of ways. A further categorization of these definitions into three approaches is proposed. Firstly, the structural method derives a firm’s technological capability from investment, production, and linkage [26]. Lall’s elaboration on technological capability has been used as a framework for evaluating technological capability [27,28,29]. In the second approach, technological capability is considered as a process that looks at the firm’s technical capability from one phase to another to generate the capability’s dynamic. Desai [30] defined technological capability as a firm’s ability to acquire, use, imitate, and develop technological innovation.
Bell and Pavitt [31] define technological capability as a firm’s ability to develop and manage technological change. These resources include skills, knowledge, experience, and institutional structures and links. Figueiredo [32] further added that it is the interrelated human capital, technology infrastructure, and organizational structures that can perform technological activities. The intangible features help the firm promote its products and increase its productivity. Many scholars have recently adopted this idea of technological capability [7,33,34,35,36]. In addition to the firm’s ability to acquire, adapt, and modify existing technologies, technological capability refers to the ability to use its technological knowledge and skills to create new products and processes and to improve existing technology so that new knowledge and skills can be generated. The mere presence of an activity does not make it a competence; only outstanding and mature procedures and skills can be regarded as a technological capability [37].
In a nutshell, technological capability is a firm’s abilities, knowledge, technologies, and structures utilized to invent, adapt, and develop new products and processes. Notably, technological capability goes beyond combining intangible assets into a firm’s advanced technology. Managing the internal tangible technology base and intangible resources, as well as external network links, is also part of technological capability. Technological capability is sophisticated, invisible, durable, and cannot be replaced, and therefore is an asset for sustainable competitive advantage. The current period of globalization and the economy has caused economic instabilities everywhere. Hence, technological capacity is vital for any firm’s technological advancement.
Technological capability has been unanimously seen as one of the most important strategic resources that enable firms to establish their long-term competitive advantage. Since technological capability is made up of technological knowledge, production skills, trade secrets, and invaluable patterns, it is hard to be imitated by competitors. Without technological capability, R&D investment per se could not provide a sustainable competitive advantage to the firm because such investment can also be easily replicated by competitors [38]. However, when the technological capability is incorporated, this intangible asset is embedded within the firm and is developed through “learning by doing”; hence, it cannot be transferred across firms or copied by rivals. Without similar experiences, competitors are unlikely to understand or replicate the firm’s technological capability.

2.3. Knowledge Management and Technological Capability

Nowadays, the source of wealth creation and performance has changed to intellectual capital, and knowledge has become a valuable asset. Gone are the days when financial capital and labor force were considered the criteria for a firm’s progress. Knowledge management recognized the importance of knowledge as a critical asset for a firm. It is an essential determinant of a firm’s success. Knowledge management helps a firm ensure that information and intellectual assets are being appropriately utilized by transforming them into value by identifying the ‘right knowledge’ to be used at the ‘right time’ [15]. Through the systematic management of this social capital, the competitive advantage of the firm is created. Eventually, knowledge management facilitates organizations to adapt to market requirement and improve efficiency and innovativeness [18]. The process of developing technological capability involved knowledge at some level, and it necessitated adopting knowledge management. Effective knowledge management also allows organizations to use their knowledge to create value for the organization, which can improve their technological capabilities by allowing them to achieve a competitive advantage in the market.
Many studies have been conducted to examine the elements that influence the development of technological capability. However, limited evidence investigates the direct effect of knowledge management on technological capability. A study that investigates the firm technological capability of a manufacturing industry in Thailand confirmed a positive association between knowledge acquisition, knowledge assimilation, knowledge transmission, and knowledge application with technological capability development [39]. In addition to this, through a qualitative interpretive approach, another study found that a prior knowledge base and technological know-how is significant in accumulating a firm’s technological capability [40]. Firms with effective knowledge management systems can create new knowledge by leveraging their internal resources and expertise. This can allow them to develop new technologies or improve existing ones, which can improve their technological capabilities. A similar study supported this argument and concluded that a firm’s prior knowledge base and effort towards intense learning must be rapidly increased to acquire technological capability in developing countries [41]. Based on these arguments, the following hypothesis is proposed for this study:
H1. 
Knowledge Management has a significant direct effect on technological capability.

2.4. Organizational Learning and Technological Capability

Organizational learning is a dynamic process of distributing, explaining, and transforming information into new knowledge and more effective organizational action [42]. The process of organizational learning acts as a catalyst for organizational change and knowledge sharing within the firm; thus, it is a strategic tool for organizational renewal. In addition to this, organizational learning also serves as a tool for the detection of errors in the firm during operation or when responding to environmental changes and correcting them by reorganizing the structure [43]. Organizational learning can help organizations adapt to changing market conditions and customer needs, which can improve their technological capability by allowing them to respond to changing demands in a timely and effective manner. Organizational learning is linked to technological capability; technological capability is a process of accumulating technical knowledge [44]. Since it is not only composed of individuals’ knowledge and skills but also the organization and purpose, it all requires learning and assimilation [29]. When an organization can effectively learn and apply new knowledge, it can improve its technological capabilities and increase its competitiveness in the market.
Several studies have attempted to establish the linkage between organizational learning and firm technological capability. A study examined how multinational companies contribute to the local industry’s technological capability and found that local suppliers improve their technological capability by learning through backward linkages. The investment by multinational companies’ subsidiaries is an essential source of knowledge acquisition for these local suppliers’ capability building [45]. In the same vein, another study proved that apart from imports of highly complex products, investment in learning, immigration, and the number of technical experts is among the essential elements of developing technological capability [46]. The international immigration factor helps in attracting brain gain and increases the productivity of a firm. Finally, the training of talents, cooperative relationships, and knowledge distribution are among the significant factors that affect technological capability [47]. Based on these arguments, this study seeks to propose the following hypothesis:
H2. 
Organizational learning has a significant direct effect on technological capability.

2.5. Organizational Innovation and Technological Capability

Organizational innovation is the implementation of a new organizational method in a firm’s practices, workplace organization, or external relationships [48]. It is a change in method for managerial practice that is new at the point of adoption. Furthermore, organizational innovation is also posited as a wide array of activities that include programs, services, devices, systems, or policies meant to facilitate and achieve the firm’s innovation outputs [49]. It is how the firm can identify a problem and collect related information to create new ideas so that a viable solution to the issue can be explored.
In the food manufacturing industry, organizational innovation may receive inadequate attention compared to technical innovation, which refers to the development of new products or processes [50]. This is because the food manufacturing industry may place a greater emphasis on technical innovation due to the competitive nature of the industry, which could make it more difficult for organizational innovation to receive the same level of attention and resources. However, organizational innovation is important because it can have a significant impact on the technological capability of a firm. This is because both organizational and environmental factors influence a firm’s innovation efforts, and organizational innovation can create an environment that is conducive to the development of new technologies [51]. In addition, the overall innovation performance of food manufacturing firms is influenced by organizational innovation, which can help firms meet customer demand, develop new organizational practices, improve external relationships, and enhance innovation activity [52]. Finally, this study argues that the development of technological capability depends on the adoption of organizational innovation through nurturing an environment that encourages the adoption and use of new technologies. By encouraging employees to experiment with new technologies and approach problems in new ways, an organization can foster a culture of innovation that supports the development of technological capability. As a result, integrating organizational innovation helps a firm stay at the forefront of technological development and be better able to adapt to changing market conditions [53]. Hence, the following hypothesis is proposed:
H3. 
Organizational innovation has a significant direct effect on technological capability.

2.6. Mediating Effect of Organizational Innovation

Innovation is assumed to be a process of achieving technological capabilities as it is considered an activity that promotes technical change. However, in many large firms, innovation fails not because they merely lack technological knowledge but because the major problem arises in the organization itself [54]. These factors can include the organization’s culture, structure, processes, and leadership. For example, an organization with a risk-averse culture may be less likely to pursue innovative ideas, while an organization with a rigid structure may struggle to implement new processes or technologies. For innovation to be successful, an organization needs to have a supportive environment that encourages and enables innovation to take place. This can involve creating a culture that values and rewards innovation, as well as establishing processes and structures that support the development and implementation of new ideas.
Research has shown that KM can have a positive impact on organizational innovation. For example, Sesay et al. [55] found that KM provides a foundation for research and analysis activities, which can support the development of new ideas and innovations. Similarly, Breznik [56] found that KM can trigger a firm’s innovation activities by providing access to information and expertise that can be used to generate new ideas. By managing knowledge effectively, organizations can better support their innovation efforts and stay competitive in the market.
A vast amount of literature has explored the link between organizational learning and firm innovation. A strong dedication to learning, open-mindedness, and a common objective lead to increased innovation in firms [57]. Furthermore, learning plays a key role because it allows individuals to improve their skill set to generate and implement organizational innovation. Learning allows the firm to establish innovative organizational methods for external relations, decision making, and diverse business activities [51].
The importance of the organizational innovation construct has traditionally been outlined as being essential for a firm’s survival and effective performance. Thus, it seems important to establish whether knowledge management and organizational learning can be considered an antecedent of organizational innovation and, consequently, to confirm whether organizational innovation acts as a mediating variable in the knowledge management, organizational learning, and technological capability linkage. Given these arguments, this study proposes that the relationships between knowledge management and organizational learning with technological capability are mediated by organizational innovation. As a result, the following hypotheses are proposed:
H4. 
Organizational innovation mediates the relationship between knowledge management and technological capability.
H5. 
Organizational innovation mediates the relationship between organizational learning and technological capability.
The proposed framework for this study is presented in Figure 1. The independent variables are knowledge management and organizational learning; organizational innovation is the mediating variable; and technological capability is the dependent variable.

3. Methods

3.1. Sample and Procedure

The study’s target population is based on the Federation of Malaysian Manufacturers (FMM) Directory of Malaysian Industries 49th Edition. The Federation of Malaysian Manufacturers (FMM) is the largest private sector economic organization in Malaysia, comprising more than 3000 manufacturing and industrial service companies of varied sizes. After scrutinizing the list, the total number of food manufacturing firms that were listed in the directory is 388 firms.
After obtaining the total population of the food manufacturing firms that were listed in the directory, the sample size for this study was determined. The sample size was drawn from the table for determining sample size by Krejcie and Morgan [58]. The table can be utilized if the population size is known. Hence, according to Kriejcie and Morgan’s sample determination, 191 firms were required as the sample. In addition to this, Hair et al. [59] proposed the minimum sample size of 100 to 150 to ensure a stable maximum likelihood estimation (MLE) solution. However, to minimize error in sampling and to take care of the non-response rate issue, Cohen et al. [60] suggested doubling the sample size. As such, we decided to use all 388 food manufacturing firms as the target sample for this research. We contacted the executive level and above employees of the firms as the respondents. Questionnaires were distributed to all 388 firms, of which 270 (69.6%) were returned after having been completed. Table 1 provides the demographic information of the participating firms in the survey.

3.2. Measures

The data were obtained using structured questionnaires (refer to Table S1). We incorporated items from previous pieces of literature for the indicated constructs of knowledge management (KM) [61], organizational learning (OL) [62], organizational innovation (OI) [63], and technological capability (TC) [64]. The instruments were developed to collect data on the determinants of the technological capability of food manufacturing firms. The items for these constructs were derived from past research since they were frequently used and had an established original composite reliability value of more than 0.7 [59]. The use of a scale with a midpoint, such as a five-point Likert scale with responses ranging from 1 = “Strongly Disagree” to 5 = “Strongly Agree”, will be more accurate and allows respondents to express their opinions [65,66].
To ensure the reliability and validity of the measures, a pilot test and exploratory factor analysis (EFA) were done before the field study. The dimensions of previous research may not be the same, especially if the present study is undertaken in different industries and environments [67]. In addition to this, the issue of common method variance, where systematic measurement error can lead to bias in the relationship between variables, may occur when the same person provides measures for both exogenous and endogenous variables. One technique for addressing this issue is the Harman single-factor test, which involves performing an exploratory factor analysis by loading all variables into one single factor and examining the unrotated factor solution [68]. In this study, the total variance extracted when all items were constrained to one factor was 44.66%, which was below the suggested threshold of 50%. Therefore, it was concluded that the collected data were free from the threat of common method bias.
This study applied structural equation modeling (SEM) to investigate the causal influence of organizational characteristics on the technological capability of manufacturing firms. This is a popular method with a wide variety of applications in many different research fields. SEM allows the researcher to assess the individual constructs, mediating and moderation effects, as well as the fitness of the overall model simultaneously [67]. Furthermore, confirmatory factor analysis in SEM improves the validity and reliability analysis of the observed variables by considering the correlated measurement errors among the response items [69,70].

4. Results

The reliability analysis was conducted to verify the instruments’ reliability for the constructs of knowledge management, organizational learning, organizational innovation, and technological capability. Cronbach’s alpha was greater than 0.7 for all constructs, according to Table 2. A Cronbach’s alpha score higher than 0.6 indicates that the instruments are reliable and should therefore be utilized in future research [67].
The measures of the skewness and kurtosis for every component are presented in Table 3. The results show that all constructs’ scores are normally distributed. Since the maximum likelihood estimator (MLE) algorithm is robust to skewed data, research must establish that the skewness value for all items does not depart from normality. Skewness values in the range of −1.5 to 1.5 are acceptable for a sample size greater than 200 [67,71].
The study needs to validate the measurement model of all latent constructs in the model for unidimensionality, validity, and reliability before modeling the structural model and executing the structural equation modeling (SEM). Confirmatory factor analysis (CFA) is a statistical technique used to assess the validity of a measurement model, which represents the relationships between observed variables and latent constructs. In CFA, all the constructs are pooled together and analyzed at once using a double-headed arrow [69]. The goal is to achieve both convergent and construct validity, which may require modifying or deleting items in the model one at a time until satisfactory fitness indexes are achieved. Figure 2 depicts the final measurement model.
The assessment for construct validity is shown in Table 4. The particular latent construct is considered valid if its fitness indices achieve the required construct validity level [59].
Furthermore, the study must assess the convergent validity and reliability using the average variance extracted (AVE) and composite reliability (CR) values. If the construct’s AVE is greater than the 0.5 thresholds, it has achieved convergent validity. However, if the CR value is higher than 0.6, the AVE value of 0.4 can be accepted, suggesting that the construct has established convergent validity [72]. To evaluate the composite reliability, the CR needs to be computed, and its value should exceed the 0.6 thresholds to be considered reliable [73,74]. The AVE and CR for all constructs are summarized in Table 5. When the requirements for unidimensionality, validity, and reliability for the measurement model are fulfilled, this CFA model may be processed for analysis in the structural equation modeling.
Figure 3 depicts the estimated results of modeling the inter-relationship among the constructs. The model has an R2 value of 0.73. Overall, the model is good since it was able to capture 73% of the information on the endogenous construct by integrating certain exogenous constructs in the model. At the same time, most fitness indices are appropriate since the values met the required level and the factor loading for all items are satisfactory (above the required 0.6). This model indicates that 73% of technological capability could be estimated by using three exogenous constructs, namely knowledge management, organizational learning, and organizational innovation, while 47% of organizational innovation could be measured using knowledge management and organizational learning.
Table 6 depicts the estimated direct effect between constructs. Organizational learning has a statistically significant direct impact on organizational innovation. The direct effect of organizational innovation on technological capability is also statistically significant. However, statistical analysis for the present study supported the hypothesis that organizational innovation has a statistically significant direct impact on technological capability (H3). However, the hypotheses about the direct effects of knowledge management (H1) and organizational learning (H2) on technological capability were not supported.
We intended to test further whether organizational innovation mediates the relationship between knowledge management, organizational learning, and technological capability. To examine the mediation effect of organizational innovation in the relationship between knowledge management and technological capability, and between organizational learning and technological capability, the study employed the procedure [69], as shown in Figure 4 and Figure 5.
The result of hypothesis testing in Figure 4 indicated that organizational innovation does not mediate the relationship between knowledge management and technological capability since the direct relationship between knowledge management and organizational innovation is not significant. Therefore, H4 is not supported.
The result of hypothesis testing in Figure 5 indicated that organizational innovation mediates the relationship between organizational learning and technological capability. Furthermore, the type of mediation here is complete mediation since the direct effect is insignificant. Hence, H5 is supported.

5. Discussion

Based on the result, we found that organizational innovation plays a significant role in the development of a firm’s technological capability, whereas organizational learning and knowledge management have a limited impact on the firm’s technological capability. This is demonstrated by the fact that organizational innovation fully mediates the relationship in the framework. In other words, this study suggests that the ability of food manufacturing firms in Malaysia to learn and absorb new knowledge may not have a direct impact on their technological capability. Nevertheless, it could influence their ability to introduce new ideas, processes, products, or services that improve their technological capability.
As noted, organizational innovation is the process by which a firm can identify problems and gather data to create new solutions. Organizational innovation practices that facilitate the absorption and integration of new knowledge may be critical for the development of the firm’s technological capability. Firms that can learn and innovate effectively may be more likely to develop and improve their technologies, while those that are not able to do so may struggle to keep pace with their rivals in the industry. This finding aligns with absorptive capacity theory, which states that a firm’s ability to learn and innovate depends on its ability to absorb and integrate new knowledge from external sources [75].
On the other hand, the limited effect of knowledge management practices in Malaysian food manufacturing firms may be the explanation for the weak association between knowledge management and technological capability. Additional factors must be taken into account, such as the lack of executive leadership, and the shortage of skilled labor, as well as other resources that are more important in the context of Malaysian food manufacturing setting [76,77,78,79]. Food manufacturing firms in Malaysia may face constraints on their resources, such as funding and time, that could limit their ability to invest in knowledge management systems [80,81]. Additionally, previous scholars had mentioned that the Malaysian manufacturers are struggling to find and hire skilled professionals, such as engineers or technologists, who could contribute to the development of new technologies or processes [82]. Consequently, this hinders the firm’s ability to learn and innovate.
In this perspective, the firm’s knowledge management practices might still be beneficial for other purposes, such as increasing the efficiency and productivity of its employees, but they might not contribute to the firm’s technological capability [83]. This result is in line with the knowledge-based view (KBV) theory of the firm. According to KBV theory, a firm’s knowledge assets, such as its technological knowledge, are a key source of competitive advantage and a key determinant of the firm’s performance. However, KBV theory also suggests that the impact of knowledge assets on performance depends on the firm’s ability to create, share, and apply these assets effectively [84].
In a nutshell, this study illustrates how critical organizational innovation is for boosting the technological capability of food manufacturing firms in Malaysia. The findings imply that organizational learning and knowledge management may not directly influence a firm’s technological capability. Nevertheless, they may be useful in helping to bring new ideas, processes, products, or services that enhance a firm’s technological capability through organizational innovation. The findings also highlight the necessity for food manufacturing firms in Malaysia to have strong organizational innovation strategies in order to remain competitive in the market.

6. Conclusions

Technological capability of food manufacturing firms is critical to improving productivity and eventually contributing to national economic growth and sustainability. This study explored the development of the technological capability of the Malaysian firms by identifying factors that may boost a firm’s technological capability. In this study, we found that organizational innovation fully mediated the relationship between organizational learning and technological capability. Overall, the findings suggest that food manufacturing firms in Malaysia that want to enhance their technological capability should focus on developing and implementing strong organizational innovation practices, in addition to other strategies that aim to promote organizational learning and innovation.

6.1. Theoretical and Practical Implications

This study provides some theoretical implications. From a knowledge-based view (KBV) perspective, we suggest that knowledge management practices alone may not have a significant impact on the technological capability of food manufacturing firms in Malaysia, as the impact of knowledge assets on a firm depends on its ability to create, share, and apply them effectively. In addition to this, we explored the development of absorptive capacity theory and supported the premise that a firm’s ability to learn and innovate depends on its ability to absorb and integrate new knowledge from external sources. In addition to this, our study expands the body of literature by suggesting a framework on the factors affecting a firm’s technological capability.
Our findings also suggest substantial practical implications that may be useful for the food manufacturing industry. Managers of the food manufacturing firms in Malaysia must focus on developing and implementing organizational innovation practices to enhance their technological capability, such as finding opportunities for new technological development, introducing new technologies or processes, and implementing strategies to absorb and integrate external knowledge. Furthermore, industry stakeholders, such as policymakers, trade associations, and industry leaders, may want to consider ways to support the development and implementation of these practices within the industry, such as through funding programs, training initiatives, or regulatory frameworks. Additionally, other factors such as leadership style and market conditions should be considered to better understand the challenges and opportunities facing these firms and develop strategies and practices to improve their technological capability.

6.2. Limitation and Suggestion for Future Research

Despite its contribution, this study has several limitations that need to be considered by future researchers. Firstly, it is imperative to note that the relationship between knowledge management, organizational learning, organizational innovation, and technological capability is complex and multifaceted. Thus, future research must consider this limitation to gain a more comprehensive understanding of the factors affecting technological capability.
Secondly, while the findings of this study provide valuable insights into the technological capability of food manufacturing firms in Malaysia, they cannot be generalized to other industries or even other manufacturing firms. Therefore, future research should involve firms from different industries to explore industry-specific characteristics and their relationship with technological capability. Furthermore, it is recommended that future research be conducted in other countries. This would provide insights into how these relationships may vary across different contexts, allowing for more nuanced and comprehensive recommendations for firms and policymakers seeking to promote technological capability and innovation.
Finally, although the potential common method bias was addressed by using Harman’s single factor test, future studies could attempt to obtain more objective and robust data from multiple informants, nested within operating units and levels, across the firms. Having only one respondent for each variable did not allow the study to test within group agreement. This could be an important issue because different employees may have different perceptions about their organizational environment [85] (Klein and House, 1995).

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/2071-1050/15/8/6365/s1, Table S1: List of constructs indicators.

Author Contributions

Conceptualization, H.S.J. and C.W.J.W.M.R.; methodology, N.A.; validation, C.W.J.W.M.R.; formal analysis, H.S.J. and N.A.; investigation, N.A.; resources, N.A.A.; data curation, N.A.; writing—original draft preparation, N.A. and N.A.A.; writing—review and editing, C.W.J.W.M.R. and H.S.J.; visualization, N.A. and N.A.A.; supervision, C.W.J.W.M.R. and H.S.J.; project administration, C.W.J.W.M.R.; funding acquisition, N.A.A. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the UMCares—Universiti Malaya Community Engagement Grant (RUU2022-CE (06)) and Universiti Malaya Research Grant—Humanities Research Cluster (RP027E-15HNE).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets generated during and/or analyzed during the current research are available from the corresponding author on reasonable request.

Acknowledgments

The authors are grateful to the UMCares—Universiti Malaya Community Engagement Grant (RUU2022-CE (06)) and Universiti Malaya Research Grant—Humanities Research Cluster (RP027E-15HNE).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Priyadarshini, A.; Rajauria, G.; O’Donnell, C.P.; Tiwari, B.K. Emerging food processing technologies and factors impacting their industrial adoption. Crit. Rev. Food Sci. Nutr. 2019, 59, 3082–3101. [Google Scholar] [CrossRef] [PubMed]
  2. Malaysian Investment Development Authority. Ideal Prospects, Immense Opportunities Diverse Resources. 2019. Available online: https://www.mida.gov.my/wp-content/uploads/2020/07/20200707102649_Food-Industry-in-Malaysia_03072020.pdf (accessed on 10 June 2021).
  3. Nordin, M.I.A.; Ghani, N.A.A.; Chong, E.; Bakar, Z.-F.A.; The BNM Quarterly Bulletin. Kuala Lumpur. 2019. Available online: https://www.bnm.gov.my/documents/20124/766180/3Q2019_fullbook_en.pdf/ec308d44-5eca-dbc6-7e8b-76789cc33c01?t=1614069407486 (accessed on 10 June 2021).
  4. Tortajada, C.; Lim, N.S.W. Food Security and COVID-19: Impacts and Resilience in Singapore. Front. Sustain. Food Syst. 2021, 5. [Google Scholar] [CrossRef]
  5. Malaysia Competition Commission. Market Review on Food Sector in Malaysia under the Competition Act 2010. 2019. Available online: https://www.mycc.gov.my/market-review/final-report-market-review-on-food-sector-under-competition-act-2010 (accessed on 10 June 2021).
  6. Olomi, P.; Akintokunbo, O.O. Technological Capability and Strategic Flexibility of Food and Beverage Manu-facturing Companies in Rivers State. Niger. Acad. Manag. J. 2022, 17, 178–189. [Google Scholar]
  7. Tseng, M.-L.; Lim, M.K.; Ali, M.H.; Christianti, G.; Juladacha, P. Assessing the sustainable food system in Thailand under uncertainties: Governance, distribution and storage drive technological innovation. J. Ind. Prod. Eng. 2022, 39, 1–18. [Google Scholar] [CrossRef]
  8. Noor Hasnan, N.Z.; Yusoff, Y.M. Short Review: Application Areas of Industry 4.0 Technologies in Food Processing Sector. In Proceedings of the 2018 IEEE 16th Student Conference on Research and Development (SCOReD), Bangi, Malaysia, 26–28 November 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 1–6. [Google Scholar] [CrossRef]
  9. Rahmat, S.; Chew, B.C.; Abdul Hamid, M.S.R. Technological achievement in the food industry in Malaysia. Food Res. 2021, 1–4. [Google Scholar] [CrossRef]
  10. Grant, R.M. Toward a knowledge-based theory of the firm. Strat. Manag. J. 1996, 17, 109–122. [Google Scholar] [CrossRef]
  11. Cohen, W.M.; Levinthal, D.A. Absorptive capacity: A new perspective on learning and innovation. Adm. Sci. Q. 1990, 35, 128–152. [Google Scholar] [CrossRef]
  12. Nanayakkara, S.M.; Wickramasinghe, V.; Samarasinghe, G.D. Role of Strategic Emotional Intelligence on Technological Capability, Technological Knowledge Management and Organisational Learning & Growth. In Proceedings of the MERCon 2018—4th International Multidisciplinary Moratuwa Engineering Research Conference, Moratuwa, Sri Lanka, 30 May 2018–1 June 2018; IEEE: Piscataway, NJ, USA; pp. 294–299. [Google Scholar] [CrossRef]
  13. Siwadi, P.; Pelser, T. Exploring Technological Capabilities To Resuscitate The Zimbabwean Manufacturing Sector. J. Appl. Bus. Res. JABR 2015, 31, 1023. [Google Scholar] [CrossRef]
  14. Wang, L.; Zheng, Y.; Huang, Z. Diverse Ties with Service Intermediaries and Established Firms’ Service Innovation: The Moderating Role of Technological Capability. In Proceedings of the 2018 Portland International Conference on Management of Engineering and Technology (PICMET), Honolulu, HI, USA, 19–23 August 2018; IEEE: Piscataway, NJ, USA; pp. 1–6. [Google Scholar] [CrossRef]
  15. Al Nawafah, S.; Nigresh, M.; Tawalbeh, A.K. The Role of Knowledge Management on Competitive Advantage in Jordan Manufacturing Companies from Employees Perspectives. Int. Bus. Res. 2019, 12, 58. [Google Scholar] [CrossRef]
  16. Rajneesh, A.; Kaur, K. Knowledge Management and Firm Performance: A Descriptive Study. Int. J. Adv. Res. Comput. Sci. Manag. Stud. 2014, 2, 198–204. [Google Scholar]
  17. Saqib, M.; Zarine, R. An empirical investigation of moderation between knowledge management and organi-sational performance. J. Leg. Ethical Regul. Issues 2018, 22, 1–6. [Google Scholar]
  18. Broekema, W.; van Kleef, D.; Steen, T. What Factors Drive Organizational Learning From Crisis? Insights From the Dutch Food Safety Services’ Response to Four Veterinary Crises. J. Contingencies Crisis Manag. 2017, 25, 326–340. [Google Scholar] [CrossRef]
  19. Kadhim, R.I.; Mohammed, M.A.; Gremikh, H.G. Empowerment as a strategy to achieve the competitive advantage of organizations: A mediating role of organizational learning. Manag. Sci. Lett. 2018, 8, 903–912. [Google Scholar] [CrossRef]
  20. Md Hasan, S.A.; Ibrahim, A. Key factors predicting firm’s technological capability of Malaysian manufactur-ing industry from technology transfer perspective. Int. J. Manag. Sci. Busi-Ness Res. 2015, 4, 37–49. [Google Scholar]
  21. Shafia, M.-A.; Mohammadi, M.; Babakhan, A.-R.; Monajemzadeh, S.S. The developmental model of technological capability in the Iranian medical equipment industries: A multi-case study. Afr. J. Sci. Technol. Innov. Dev. 2015, 7, 101–110. [Google Scholar] [CrossRef]
  22. Da Silva, M.P.; Jannuzzi, G.D.M. Technology transfer and technological capability: The regulator’s role in the adoption of solar photovoltaics in Brazil. Int. J. Technol. Learn. Innov. Dev. 2019, 11, 119. [Google Scholar] [CrossRef]
  23. Hansen, U.E.; Lema, R. The co-evolution of learning mechanisms and technological capabilities: Lessons from energy technologies in emerging economies. Technol. Forecast. Soc. Chang. 2019, 140, 241–257. [Google Scholar] [CrossRef]
  24. Zahra, S.A.; George, G. Absorptive Capacity: A Review, Reconceptualization, and Extension. Acad. Manag. Rev. 2002, 27, 185. [Google Scholar] [CrossRef]
  25. Nonaka, I.; Takeuchi, H.; Umemoto, K. A theory of organizational knowledge creation. Int. J. Technol. Manag. 1996, 11, 833–845. [Google Scholar]
  26. Lall, S. Technological capabilities and industrialization. World Dev. 1992, 20, 165–186. [Google Scholar] [CrossRef]
  27. Dutrénit, G.; Natera, J.M.; Anyul, M.P.; Vera-Cruz, A.O. Development profiles and accumulation of technological capabilities in Latin America. Technol. Forecast. Soc. Chang. 2019, 145, 396–412. [Google Scholar] [CrossRef]
  28. Reichert, F.M.; Beltrame, R.S.; Corso, K.B.; Trevisan, M.; Zawislak, P.A. Technological Capability’s Predictor Variables. J. Technol. Manag. Innov. 2011, 6, 14–25. [Google Scholar] [CrossRef]
  29. Solis-Quinteros, M.M.; Avila-Lopez, L.A.; Zayas-Márquez, C.; Carrillo-Gutierrez, T.; Arredondo-Soto, K.C. Analysis of the Technological Capability of Linking SMEs in the Electronic Sector to Integrate into the Maquiladora Industry Electronic Sector in Tijuana, Baja California, Mexico. In Advances in Manufacturing, Production Management and Process Control; Karwowski, W., Trzcielinski, S., Mrugalska, B., Eds.; Springer: Cham, Switzerland, 2020; pp. 376–387. [Google Scholar] [CrossRef]
  30. Desai, A.V. India’s technological capability: An analysis of its achievements and limits. Res. Policy 1984, 13, 303–310. [Google Scholar] [CrossRef]
  31. Bell, M.; Pavitt, K. Accumulating Technological Capability in Developing Countries. World Bank Econ. Rev. 1992, 6, 257–281. [Google Scholar] [CrossRef]
  32. Figueiredo, P.N. Micro-level technological capability accumulation in developing economies: Insights from the Brazilian sugarcane ethanol industry. J. Clean. Prod. 2017, 167, 416–431. [Google Scholar] [CrossRef]
  33. Ariana, L. Coevolution of technological capabilities and regulation in Indonesian herbal medicine sector. Int. J. Technol. Policy Manag. 2019, 19, 14. [Google Scholar] [CrossRef]
  34. Fitz-Oliveira, M.; Tello-Gamarra, J. Technological trajectory based on micro level technological capability: Evidence from the Brazilian rice industry. Int. J. Innov. Stud. 2022, 6, 196–215. [Google Scholar] [CrossRef]
  35. Martins, E.S.; Junior, D.F.L.D.C.; Miura, M.N.; De Abreu, J.C.; Martino, F.A. Dynamic technological capability of a university as source of academic performance. Int. J. Bus. Innov. Res. 2019, 19, 183. [Google Scholar] [CrossRef]
  36. Tumelero, C.; Sbragia, R.; Borini, F.M.; Franco, E.C. The role of networks in technological capability: A technology-based companies perspective. J. Glob. Entrep. Res. 2018, 8, 7. [Google Scholar] [CrossRef]
  37. Aeron, P.; Jain, R. A study on technological capability among product-based telecom start-ups in India: Role of technological learning and bricolage. Int. J. Technol. Learn. Innov. Dev. 2015, 7, 336. [Google Scholar] [CrossRef]
  38. Fung, M.K. Fraudulent Financial Reporting and Technological Capability in the Information Technology Sector: A Resource-Based Perspective. J. Bus. Ethic. 2019, 156, 577–589. [Google Scholar] [CrossRef]
  39. Chantanaphant, J.; Noor Un Nabi, M.; Dornberger, U. The effect of technological capability on the performance of SMEs in Thailand. Macrotheme Rev. 2013, 2, 16–26. Available online: https://macrotheme.com/yahoo_site_admin/assets/docs/3JirayuthMR24pdf.13191151.pdf (accessed on 13 November 2020).
  40. Qiu, P.; Nunes, B.; Vaidya, K.; van de Kaa, G.; Greeven, M. Technological capabilities development model in Chinese energy service companies. J. Clean. Prod. 2022, 330, 129551. [Google Scholar] [CrossRef]
  41. Adepoju, A.O.; Akarakiri, J.; Akinbami, J.F.-K. Framework for Building Technological Capability in the Nigerian Solar Energy Industry Using Structural Equation Modelling. In Proceedings of the 15th Globelics International Conference, Athens, Greece, 11–13 October 2017. [Google Scholar]
  42. Zhu, C.; Liu, A.; Wang, Y. Integrating organizational learning with high-performance work system and entrepreneurial orientation: A moderated mediation framework. Front. Bus. Res. China 2019, 13, 11. [Google Scholar] [CrossRef]
  43. Narsa, I.M.; Basuki, P.; Hons, M.C. The effect of market orientation, innovation, organizational learning and entre-preneurship on firm performance. J. Entrep. Educ. 2019, 22, 1–13. [Google Scholar]
  44. Rasiah, R.; Kimura, F.; Oum, S. Epilogue: Implications for promoting firm-level technological capabilities. Asia Pac. Bus. Rev. 2016, 22, 193–200. [Google Scholar] [CrossRef]
  45. Zakariah, A. Technological Capability Building through Backward Linkages in the Malaysian Petrochemical Industry/Zakariah Abdullah. 2012. Available online: https://pendeta.um.edu.my/client/en_US/default/search/results/?qu=Technological+capability+building+through+backward+linkages+in+the+Malaysian+petrochemical+industry&te= (accessed on 10 June 2021).
  46. Hernández, C.C.P. Drivers of technological capability in Mexico: The mesoeconomic factors that impulse the techno-scientific products. Contaduría Adm. 2019, 65, 158. [Google Scholar] [CrossRef]
  47. Lin, F.-J.; Lai, C. Key factors affecting technological capabilities in small and medium-sized Enterprises in Taiwan. Int. Entrep. Manag. J. 2021, 17, 131–143. [Google Scholar] [CrossRef]
  48. OECD/Eurostat. The Measurement of Scientific and Technological Activities, Communities. In Oslo Manual: Guidelines for Collecting and Interpreting Innovation Data, 3rd ed.; OECD Publishing: Paris, France, 2005. [Google Scholar] [CrossRef]
  49. Chen, Q.; Wang, C.-H.; Huang, S.-Z. Effects of organizational innovation and technological innovation capabilities on firm performance: Evidence from firms in China’s Pearl River Delta. Asia Pac. Bus. Rev. 2020, 26, 72–96. [Google Scholar] [CrossRef]
  50. Purba, H.; Syamsul Maarif, M.; Yuliasih, I.; Hermawan, A. Innovation Typology in Food Industry Sector: A Literature Review. Int. J. Mod. Res. Eng. Technol. IJMRET 2018, 3, 8–19. [Google Scholar]
  51. Chung, D.-B.; Kim, B. An Analysis on the Role of Organizational Innovation for Enhancing Firms’ Innovation Performance: Focusing on the Manufacturing Industry in South Korea. In Proceedings of the 2017 Portland International Conference on Management of Engineering and Technology (PICMET), Portland, OR, USA, 9–13 July 2017; IEEE: Piscataway, NJ, USA; pp. 1–5. [Google Scholar] [CrossRef]
  52. Ciliberti, S.; Carraresi, L.; Bröring, S. What drives marketing and organizational innovation in the food in-dustry? A comparison between Italy and Germany. In System Dynamics and Innovation in Food Networks; Igls, Austria, 2017; pp. 177–188. [Google Scholar] [CrossRef]
  53. Chaubey, A.; Sahoo, C.K. Enhancing organizational innovation in Indian automobile industry. Int. J. Innov. Sci. 2019, 11, 82–101. [Google Scholar] [CrossRef]
  54. Bernat, S.; Karabag, S.F. Accumulating technological capabilities through R&D projects: Studies on the Brazilian defence industry. Int. J. Technol. Learn. Innov. Dev. 2018, 10, 203. [Google Scholar] [CrossRef]
  55. Sesay, B.; Yulin, Z.; Wang, F. Does the national innovation system spur economic growth in Brazil, Russia, India, China and South Africa economies? Evidence from panel data. S. Afr. J. Econ. Manag. Sci. 2018, 21, 12. [Google Scholar] [CrossRef]
  56. Breznik, K. Knowledge Management—from its Inception to the Innovation Linkage. Procedia-Soc. Behav. Sci. 2018, 238, 141–148. [Google Scholar] [CrossRef]
  57. Ma’, A.R.; Toufi, N.; Tajeddini, K. The impact of learning orientation on entrepreneurial orientation and innovation in small-sized business firms. Middle East J. Manag. 2015, 2, 252. [Google Scholar] [CrossRef]
  58. Krejcie, R.V.; Morgan, D.W. Determining Sample Size for Research Activities. Educ. Psychol. Meas. 1970, 30, 607–610. [Google Scholar] [CrossRef]
  59. Hair, J.F.; Black, W.C.; Babin, B.J.; Anderson, R.E. Multivariate Data Analysis, 7th ed.; Pearson: New York, NY, USA, 2010. [Google Scholar]
  60. Cohen, L.; Manion, L.; Morrison, K. Research Methods in Education, 6th ed.; Routledge: New York, NY, USA, 2017; p. 475. [Google Scholar]
  61. Rasula, J.; Vuksic, V.B.; Stemberger, M.I. The impact of knowledge management processes on organisational performance. Econ. Bus. Rev. 2012, 14, 147–168. [Google Scholar] [CrossRef]
  62. Tohidi, H.; Mohsen Seyedaliakbar, S.; Mandegari, M.; Seyedaliakbar, S.M.; Mandegari, M. Organizational learning measurement and the effect on firm innovation. J. Enterp. Inf. Manag. 2012, 25, 219–245. [Google Scholar] [CrossRef]
  63. Wang, C.L.; Ahmed, P.K. The development and validation of the organisational innovativeness construct using confirmatory factor analysis. Eur. J. Innov. Manag. 2004, 7, 303–313. [Google Scholar] [CrossRef]
  64. Latip, N.A.M. The Impact of Technological Capability on Power, Trust and Inter-Firm Relationship Performance. Ph.D. Thesis, University of Southern Queensland, Queensland, Australia, 2012. [Google Scholar]
  65. Elmore, P.B.; Beggs, D.L. Salience of concepts and commitment to extreme judgments in the response patterns of teachers. Educ. Chula Vista 1975, 95, 325–330. [Google Scholar]
  66. Schuman, H.; Presser, S. Questions and Answers in Attitude Surveys: Experiments on Question Form, Wording, and Context; SAGE Publications: Los Angeles, CA, USA, 1996. [Google Scholar]
  67. Awang, Z. Research Methodology and Data Analysis, 2nd ed.; Universiti Teknologi MARA Press: Shah Alam, Malaysia, 2014. [Google Scholar]
  68. Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.-Y.; Podsakoff, N.P. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J. Appl. Psychol. 2003, 88, 879–903. [Google Scholar] [CrossRef]
  69. Awang, Z.; Hui, L.S.; Zainudin, N.F.S. Pendekatan Mudah SEM: Structural Equation Modelling; MPWS Rich Resources Sdn Bhd: Bangi, Malaysia, 2018. [Google Scholar]
  70. Jenatabadi, H.S.; Radzi, C.W.J.W.M.; Azizan, S.A.; Hasbullah, M.B.; Mamat, M.Z.; Babashamsi, P. Knowledge Management in Collaborative Manufacturing Food Companies Performances: Twin Impacts of Learning and Innovation. Mod. Appl. Sci. 2016, 10, 161. [Google Scholar] [CrossRef]
  71. Awang, A.H.; Sapie, N.M.; Hussain, M.Y.; Ishak, S.; Yusof, R. Nurturing innovative employees: Effects of organisational learning and work environment. Econ. Res.-Ekon. Istraživanja 2019, 32, 1152–1168. [Google Scholar] [CrossRef]
  72. Fornell, C.; Larcker, D. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  73. Noor, N.M.; Aziz, A.A.; Mostapa, M.R.; Awang, Z. Validation of the Malay Version of the Inventory of Functional Status after Childbirth Questionnaire. BioMed Res. Int. 2015, 2015, 1–10. [Google Scholar] [CrossRef]
  74. Yahaya, T.A.B.; Idris, K.; Suandi, T.; Ismail, I.A. Adapting instruments and modifying statements: The confirmation method for the inventory and model for information sharing behavior using social media. Manag. Sci. Lett. 2018, 8, 271–282. [Google Scholar] [CrossRef]
  75. Li, C.; Sun, L.-Y.; Dong, Y. Innovating via building absorptive capacity: Interactive effects of top management support of learning, employee learning orientation and decentralization structure. Creativity Innov. Manag. 2018, 27, 431–443. [Google Scholar] [CrossRef]
  76. Ambad, S.N.A.; Andrew, J.V.; Awang Amit, D.H.D. Growth Challenges of SMEs: Empirical Evidence in Sabah, Malaysia/Sylvia Nabila Azwa Ambad, Jasmine Vivienne Andrew and Dayang Haryani Diana Awang Amit. 2020. Available online: https://ir.uitm.edu.my/43932/1/43932.pdf (accessed on 10 June 2021).
  77. Burhanuddin, M.; Arif, F.; Azizah, V.; Prabuwono, A.S. Barriers and Challenges for Technology Transfer in Malaysian Small and Medium Industries. In Proceedings of the 2009 International Conference on Information Management and Engineering, Kuala Lumpur, Malaysia, 3–5 April 2009; IEEE: Piscataway, NJ, USA, 2009; pp. 258–261. [Google Scholar] [CrossRef]
  78. Ting, I.W.K.; Sui, H.J.; Kweh, Q.L.; Nawanir, G. Knowledge management and firm innovative performance with the moderating role of transformational leadership. J. Knowl. Manag. 2021, 25, 2115–2140. [Google Scholar] [CrossRef]
  79. van Zyl, W.R.; Henning, S.; van der Poll, J.A. A Framework for Knowledge Management System Adoption in Small and Medium Enterprises. Computers 2022, 11, 128. [Google Scholar] [CrossRef]
  80. Abdul Razak, D.; Abdullah, M.A.; ERSOY, A. Small Medium Enterprises (SMES) in Turkey and Malaysia: A Comparative Discussion on Issues and Challenges. Int. J. Bus Econs. Law 2018, 15, 1–10. [Google Scholar]
  81. Alam, S.S.; Jani, M.F.; Senik, Z.C.; Domil, A.K.A. Assessing Barriers of Growth of Food Processing SMIs in Malaysia: A Factor Analysis. Int. Bus. Res. 2011, 4, 252–259. [Google Scholar] [CrossRef]
  82. Mohamad, A.; Mustapa, A.N.; Razak, H.A. An Overview of Malaysian Small and Medium Enterprises: Contributions, Issues, and Challenges. In Modeling Economic Growth in Contemporary Malaysia; Emerald Publishing Limited: Bingley, UK, 2021; pp. 31–42. [Google Scholar] [CrossRef]
  83. Abdullah, N.N.; Othman, M. Examining the Effects of Intellectual Capital on the Performance of Malaysian Food and Beverage Small and Medium-Sized Enterprises. Int. J. Civ. Eng. Technol. IJCIET 2019, 10, 135–143. [Google Scholar]
  84. Amin, S.; Usman, M.; Sohail, N.; Aslam, S. Relationship between intellectual capital and financial perfor-mance: The moderating role of knowledge assets. Pak. J. Commer. Soc. Sci. PJCSS 2018, 12, 521–547. [Google Scholar]
  85. Klein, K.J.; House, R.J. On fire: Charismatic leadership and levels of analysis. Leadersh. Q. 1995, 6, 183–198. [Google Scholar] [CrossRef]
Sustainability 15 06365 g001
Figure 1. The conceptual framework.
Figure 1. The conceptual framework.
Sustainability 15 06365 g001
Sustainability 15 06365 g002
Figure 2. The final measurement model for pooled-CFA combining all the constructs.
Figure 2. The final measurement model for pooled-CFA combining all the constructs.
Sustainability 15 06365 g002
Sustainability 15 06365 g003
Figure 3. Structural model of knowledge management, organizational learning, organizational innovation, and technological capability.
Figure 3. Structural model of knowledge management, organizational learning, organizational innovation, and technological capability.
Sustainability 15 06365 g003
Sustainability 15 06365 g004
Figure 4. The mediation testing procedure for KM—OI—TC.
Figure 4. The mediation testing procedure for KM—OI—TC.
Sustainability 15 06365 g004
Sustainability 15 06365 g005
Figure 5. The mediation testing procedure for OL—OI—TC.
Figure 5. The mediation testing procedure for OL—OI—TC.
Sustainability 15 06365 g005
Table 1. Demographic profiles.
Table 1. Demographic profiles.
DemographicCategoriesF%
Firm Size≤75 employees18970.0
75 ≤ 200 employees5620.7
≥200 employees259.3
Firm Age≤3 years7527.8
3 ≤ 10 years12044.4
11 ≤ 20 years5018.5
≥20 years 259.3
Table 2. The reliability assessment for all constructs.
Table 2. The reliability assessment for all constructs.
ConstructNo. of ItemsCronbach’s Alpha
Technological Capability110.922
Knowledge Management80.795
Organizational Learning140.775
Organizational Innovation110.909
Table 3. The assessment of normality for every component.
Table 3. The assessment of normality for every component.
VariableMinMaxSkewnessc.r.Kurtosisc.r.
TC161.0005.000−0.114−0.762−0.676−2.266
TC151.0005.000−0.324−2.173−0.403−1.350
TC121.0005.000−0.103−0.688−0.789−2.646
TC111.0005.000−0.444−2.977−0.010−0.033
TC81.0005.000−0.034−0.231−0.060−0.203
OI141.0005.000−0.159−1.066−0.501−1.679
OI81.0005.0000.0590.397−0.494−1.657
OI71.0005.000−0.138−0.927−0.439−1.472
OI61.0005.000−0.371−2.488−0.379−1.270
OI31.0005.000−0.223−1.494−0.530−1.776
OI21.0005.000−0.225−1.512−0.462−1.550
OI11.0005.000−0.109−0.732−0.738−2.476
OL221.0005.000−0.222−1.492−0.506−1.698
OL211.0005.000−0.756−5.0690.3091.037
OL201.0005.000−1.016−6.8141.0243.435
OL131.0005.000−0.265−1.775−0.327−1.098
OL21.0005.000−0.262−1.756−0.246−.826
KM81.0005.000−0.196−1.315−0.644−2.161
KM71.0005.000−0.699−4.6920.2070.696
KM61.0005.000−0.646−4.3300.2710.908
KM32.0005.000−0.714−4.7860.1140.382
Multivariate 62.60516.549
Table 4. The fitness indices for the measurement model.
Table 4. The fitness indices for the measurement model.
CategoryFit IndexIndex ValueComments
Absolute FitRMSEA0.085The required level is attained
Incremental FitCFI0.900The required level is attained
Parsimonious FitChiSq/df2.953The required level is attained
Table 5. The summary for AVE and CR.
Table 5. The summary for AVE and CR.
ConstructItemsFactor LoadingCR
(above 0.6)		AVE
(above 0.5)
KMKM30.830.8050.511
KM60.73
KM70.66
KM80.62
OLOL20.610.8180.479
OL130.59
OL200.74
OL210.74
OL220.75
OIOI10.730.8970.556
OI20.77
OI30.69
OI60.63
OI70.78
OI80.83
OI140.77
TCTC80.630.8900.621
TC110.78
TC120.84
TC150.84
TC160.83
Table 6. The regression path coefficient and its significance.
Table 6. The regression path coefficient and its significance.
ConstructPathConstructEstimatep-ValueResult
Organizational Innovation←Knowledge Management−0.070.552Not Sig
Organizational Innovation←Organizational Learning0.760.001Sig
Technological Capability←Knowledge Management0.150.106Not Sig
Technological Capability←Organizational Learning0.010.958Not Sig
Technological Capability←Organizational Innovation0.760.001Sig
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Jenatabadi, H.S.; Radzi, C.W.J.W.M.; AbdManap, N.; Abdullah, N.A. Factors That Boost the Technological Capability of Malaysian Food Manufacturing Industry. Sustainability 2023, 15, 6365. https://doi.org/10.3390/su15086365

AMA Style

Jenatabadi HS, Radzi CWJWM, AbdManap N, Abdullah NA. Factors That Boost the Technological Capability of Malaysian Food Manufacturing Industry. Sustainability. 2023; 15(8):6365. https://doi.org/10.3390/su15086365

Chicago/Turabian Style

Jenatabadi, Hashem Salarzadeh, Che Wan Jasimah Wan Mohamed Radzi, Nursyakirah AbdManap, and Nor Aishah Abdullah. 2023. "Factors That Boost the Technological Capability of Malaysian Food Manufacturing Industry" Sustainability 15, no. 8: 6365. https://doi.org/10.3390/su15086365

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop