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ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.21 No.1 pp.43-57
DOI : https://doi.org/10.7232/iems.2022.21.1.043

Identifying Driving Factors of Technological Innovation to Create Sustainable Value in Metal Manufacturing SMEs

Ni Luh Putu Hariastuti*, Pratikto, Purnomo Budi Santoso, Ishardita Pambudi Tama
Departement of Industrial Engineering, Institut Teknologi Adhi Tama Surabaya, Indonesia
Departement of Mechanical Engineering, Brawijaya University, Indonesia
Departement of Industrial Engineering, Brawijaya University, Indonesia
*Corresponding Author, E-mail: putu_hrs@itats.ac.id
November 11, 2020 August 12, 2021 January 17, 2022

ABSTRACT


One of the keys to the company’s success in achieving sustainability is the application of technological innovation. Technological innovation plays a crucial role in creating manufacturing values and excellence. It is necessary to understand what are the key drivers of implementing technological innovations that can support the achievement of competitive advantage and sustainability in manufacturing companies, including Small and Medium Enterprises (SMEs). With all the limited capacities and resources, technological innovation becomes challenging for SMEs, especially to deal with dynamics and uncertainties in the business environment. This study aims to determine the key drivers of the application of technological innovation in manufacturing SMEs to support the achievement of sustainable competitive advantage. The case study was conducted in metal manufacturing SMEs in Indonesia. This study uses Interpretive Structural Modeling (ISM) in determining the contextual relationship between the identified key drivers. Based on the results, it is known that among the eleven key drivers, three drivers belong to the dependent cluster, four belong to the linkage cluster, and the rests belong to the independent cluster. In addition, the results of this study show that factors related to management support and commitment, government regulations, collaborative support, as well as the application of artificial intelligence, are the main key drivers that need to be prioritized for the successful application of technological innovation towards sustainability. The findings of this study are intended to assist managers and practitioners, especially those engaged in metal manufacturing SMEs, to develop a strategic plan for creating sustainable manufacturing values, in the context of technological innovation.



초록


    1. INTRODUCTION

    For manufacturing companies, the ability to survive and sustain in such a dynamic and uncertain business environment depends on the company’s ability to adapt to innovation and technology fast (Digalwar et al., 2015). A company’s ability to face the challenges is essential in terms of value creation and achievement of sustainable manufacturing excellence. This becomes a competitive advantage and may lead them to win the market competition (Hariastuti et al., 2018). The rapid development in technology has provided new opportunities for companies to increase competitiveness towards sustainable development (Jasiulewicz - Kaczmarek and Gola, 2019). In this case, technology affects and interacts with the three dimensions of sustainability, including the economic, environmental, and social dimensions. Other studies that discussed the relevant role of technology development in achieving sustainable manufacturing are also emphasized by (Seliger, 2012;Tiengtavaj et al., 2017;Dubey et al., 2019;Niaki et al., 2019;Liu et al., 2020). Manufacturing companies are required to improve their technological innovation to achieve cost efficiency. However, this becomes quite challenging, especially for SMEs, due to their limited resources and capacity (Vinodh and Jayakrishna, 2014).

    Many manufacturing SMEs in Indonesia, particularly those engaged in metal processing, are facing problems related to technological limitations. Whereas, this laborintensive industry is a basis of regional economic growth in Indonesia. Moreover, the global market phenomenon has led to an increase in imports with more competitive products entering and competing in the local market. Although Indonesian metal manufacturing SMEs can produce products according to market demand, however, their bargaining power is relatively low because they have not been able to meet the expected product standards. One of the reasons is the lack of technological innovation in their manufacturing activities. It becomes a big challenge for SMEs to implement technological innovation to achieve sustainable manufacturing values.

    In previous research, studies were conducted to identify the obstacles of achieving sustainable manufacturing values as well as strategy formulation and solution suggestions (Bhanot et al., 2017;Hamalainen et al., 2018;Caldera et al., 2019;Roca and O’Sullivan, 2020). This study aims to identify what factors are the key drivers of the application of technological innovation in metal manufacturing SMEs. The Interpretive Structural Modeling (ISM) approach was carried out to learn more about the contextual relationship between factors. In addition, an analysis on the indirect relationship between factors was also done using the Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) method. Finally, strategies related to sustainable manufacturing value creation in metal manufacturing SMEs were formulated based on the ISM results.

    This paper is organized as follows. Section 1 states the background and significance of the study. Section 2 gives a brief review of the related research area, along with identification and descriptions of drivers in the previous research. Section 3 explains the materials, method, and procedure used to conduct the research. Results, discussion, and several key findings are explained in Section 4. Further analysis of the MICMAC clustering is also discussed in Section 4. Finally, Section 5 concludes the study and provides some research opportunities and possible developments.

    2. LITERATURE REVIEW

    Research on sustainable manufacturing and value creation has been developed by many researchers. Bhanot et al. (2017) propose a comprehensive sustainability framework to reduce manufacturing performance barriers. The framework is quite general and only designed from the point of view of researchers and practitioners. Different types of manufacturing industries will certainly have different characteristics. The sustainability framework needs to be made more specifically for certain manufacturing fields so that it can be closer to real problems. Research conducted by Hamalainen et al. (2018) emphasizes several factors that become manufacturing constraints at the level of individual production and social manufacturing. In his research, individual factors are known to be the main obstacle in improving sustainable manufacturing performance. According to Caldera et al. (2019), the application of lean thinking is one of the transition processes in overcoming obstacles to sustainable business practices. Research conducted by (Roca and O’Sullivan, 2020) shows that obstacles related to the misalignment of public support programs need to be anticipated. Most of the existing research has not involved new technological innovation strategies to create manufacturing value in achieving sustainable competitiveness. This study is conducted to provide an overview of the critical factors and their relationships, which will later be used in the formulation of technological innovation strategies for sustainability competitiveness in manufacturing SMEs.

    Studies on smart technology have attracted many researchers. The availability of existing technology, of various levels and complexity, can provide a factual basis for companies to create value in carrying out the process of sustainability improvement (Ghobakhloo, 2018;Barbu and Militaru, 2019;Demartini et al., 2019;Gillani et al., 2020). New approaches to technological developments are being studied and applied in various industries. Technological developments such as the Internet of Things (IoT), Cyber-Physical System (CPS), Cloud Computing (CC), Meaningful Intelligence (AI), Big Data Analytics (BDA), Digital Twin (DT), etc., can advance and develop sustainable manufacturing activities throughout all stages of the product life cycle (Ren et al., 2019). The application of industry 4.0 can be used as a control system to achieve more productive activities (Di Nardo, 2020). Besides, the need for technological innovations such as the automation process of machines and equipment, the application of additive manufacture (AM), and the use of information technology (IT) can contribute to advancing the science of digitizing technology and assisting the industry in making progress towards a sustainable industry (Liu et al., 2020) and (Zhang et al., 2020). Bagautdinova and Kadochnikova (2020) also emphasized that the important role of technological innovation in terms of digital transformation can ensure sustainable macroeconomic growth. The role of new technology in manufacturing value creation activities towards sustainability shows the importance of driving factors in supporting innovative technology in the manufacturing industry.

    Many researchers agree that ISM is suitable to identify the relationship between the elements of factors that make up a structural problem or objective (Warfield, 1974;Jhawar and Garg, 2018). In the area of sustainable manufacturing, ISM has been used in the process of improving the management performance of recycling vehicles (Zhou et al., 2019), as well as the application of lean methodologies and green manufacturing (Chaple et al., 2018;Seth et al., 2018). According to Orji (2019), the framework's inefficiency and the low level of waste management are the main obstacles in the path to sustainability. Likewise, focusing on the organizational constraints in the steel industry, ISM analysis shows that low management awareness and conflict of interest in the organization are significant constraints in the energy efficiency process (Soepardi and Thollander, 2018). The application of ISM in India’s oil and gas sector concludes that uncertainty, market competition, and limited resources are the main obstacles in implementing sustainable activities (Raut et al., 2018). ISM is powerful to raise critical factors and study the relationship and interrelationships between the factors and elements of a system. The method can provide and classify factors into several hierarchical levels, which are categorized based on the magnitude of the driving force and the dependency that occurs. Therefore, in this study, the authors use the ISM methodology to study and improve manufacturing sustainable value creation in SMEs.

    2.1 Utilization and Application of Smart Technology

    Smart technology has a role in the achievement of cleaner and green production. Data and information related to the product life cycle are necessary due to the increasing application of smart technologies (Lou et al., 2020). The term smart sustainable manufacturing (SSM) was born because of the application of smart manufacturing technology and big data analysis that were carried out together to create manufacturing sustainability value (Zhang et al., 2017;Ren et al., 2019). The utilization of smart technology in manufacturing activities can help company managers to make improvements, including setting up productive machinery (Di Nardo, 2020); corrective and predictive maintenance of production equipment; production and supply chain performances; the design of services and product prices; information reporting processes and workforce welfare (Zancul et al., 2016;Tao et al., 201).

    2.2 Productivity and Profitability

    Implementing sustainable manufacturing and carrying out continuous improvement activities can be a driver for increasing the company's productivity. The company's commitment to building and maintaining the company's image is also one of the keys to the company's success in increasing its expected profitability (Moldavska and Welo, 2019).

    2.3 Product and Service Quality

    The rapid development of information and communication technology eventually creates the concept of Smart Manufacturing (SM) (Ren et al., 2019;Wang et al., 2016). This new technology provides opportunities for industries to integrate their products and services to implement sustainable production strategies (Zhang et al., 2020). The existence of technological innovation makes it easier for managers to develop their product and service designs, based on customers’ needs and requirements.

    2.4 Quality of the Production Process

    In manufacturing activities, technological innovation helps company managers to improve production processes. Improvements in terms of machinery and equipment settings; corrective and predictive maintenance; raw material supply management; product quality; and reporting and documentation processes are needed to optimize and balance production and service performance. More accurate predictions on maintenance, optimization of production and service schedules, as well as trade-offs between product performance and the environment can be optimized with the application of more precise and better technologies (Zancul et al., 2016). In addition, the application of modern technology helps companies to evaluate energy consumption and efficiency, as well as identify failures that cause high CO2 emissions or other environmental impacts (Rødseth et al., 2017). Using modern technologies, manufacturing companies can perform realtime preventive maintenance, help improve the economic performance of manufacturing processes, and provide process stability. In addition, technological advances make it easier for consumers to get better information, this increases the demand for more economical products with higher quality and shorter order fulfillment times (Horváth and Szabó, 2019). To meet this consumer demand, companies need to develop their production systems by utilizing new manufacturing technologies. Digital manufacturing technology is one of the promising options for efficiency, flexibility, cost-effectiveness, speed, and increased customization (Dalenogare et al., 2018;Nara et al., 2021).

    2.5 Reliability of Production Equipment and Technology

    The application of advanced technology provides new opportunities for companies to increase economic growth and competitiveness (Bagautdinova and Kadochnikova, 2020). In manufacturing activities, the maintenance technology function is expected to support sustainable value creation and contribute to both the economic, environmental, and social dimensions. The application of Maintenance Technology 4.0 can optimize the reliability maintenance of production machinery and equipment by maximizing their useful life while avoiding unplanned downtime. It is also expected to minimize downtime, improve safety, minimize energy and resource consumption, and save costs (Jasiulewicz-Kaczmarek and Gola, 2019). The existence of SM supports the fact of a productive production environment of machines and raw materials in minimizing waste, product defects, and downtime associated with smart devices (Bokrantz et al., 2020). In this system, process efficiency is optimized through process automation of machines and equipment and production optimization, which is implemented by human resources through IoT infrastructure (Wang et al., 2016) and (Masood and Sonntag, 2020).

    2.6 Labor’s Quality and Productivity

    Total employee involvement in developing labor’s quality and productivity supports the achievement of sustainable manufacturing performance. The support of advanced technology in the production process encourages the workforce to be more effective and innovative by continuing to learn and adapt to the technology through a human resource development program (Di Nardo, 2020). A qualified workforce tends to support improving work performance and productivity towards manufacturing excellence (Ojha et al., 2014).

    2.7 Promotion and Company’s Reputation

    Management awareness and support in building a company image are reflected in the implementation of technology innovation activities in every aspect of the manufacturing system. Companies in maintaining their business reputation must ensure that they are leaders from competitors in offering products and services (Moldavska and Welo, 2019). A good company reputation is a guarantee for promotional activities in expanding marketing, supported by the presence of appropriate marketing policies and advertising methods and assisted by modern technology facilities and infrastructure.

    2.8 Collaboration and Partnership

    The application of innovative technology policies to revitalize global collaboration and partnerships can increase management awareness in understanding the importance of sustainability (Hariastuti et al., 2021). Collective decisions with the academic support of technology capital can adequately express the cooperation between various stakeholders and collectively create a strong foundation to achieve a sustainable competitive advantage (Januškaitė and Užienė, 2018). Technology involvement can accelerate and enhance sustainable development through the ease of access obtained in terms of information and services, increased connectivity, organization and network, efficiency, increased productivity, and efficiency of resources (Secundo et al., 2020). The existence of the right technology to support efforts to build a culture of collaboration and networking in the field of company development, including efforts to increase the adoption of technology enhancement and education for human resources.

    2.9 Government Regulations and Supports.

    Government supports through a simpler, facilitative, efficient, and effective regulatory framework help to protect and facilitate companies in carrying out their business. Regulations related to employment, collaborative support and partnerships, venture capital, and straightforward licensing are the main drivers for industries to increase their pace towards manufacturing excellence (Ojha et al., 2014). Strict environmental and government regulations can limit the manufacturing industry from carrying out production operations with negative environmental impacts. More efficient operations need to start by integrating government regulations that support sustainability with economic benefits (Malek and Desai, 2019).

    2.10 Top Management Support and Commitment

    Management’s commitment to creating a reliable and adaptive organizational culture will support the achievement of manufacturing excellence. This includes creating manufacturing activities that are based on leadership, strategic development, employee involvement, production processes, and equipment improvement, as well as a systematic performance appraisal system (Ojha et al., 2014). The need for company support and commitment to maintaining customer satisfaction, implementing TQM, and implementing lean and straightforward management principles will support sustainable manufacturing operations (Bhanot et al., 2017).

    2.11 Strategies for Achieving Competitive Advantage

    Information technology enables the acquisition of information, both internal and external, to be easier and faster. Information about the financial situation, production costs, product effectiveness and quality, competitors, customers, suppliers, and the government, helps companies develop themselves and can encourage them to gain a sustainable competitive advantage (Barbu and Militaru, 2019).

    3. MATERIALS AND METHODS

    This research focuses on identifying and analyzing the critical factors that drive the application of technological innovation using ISM methodologies. ISM identifies and helps to understand the reciprocal relationship between variables or factors. ISM describes the relationship of elements with direct connections in a comprehensive and systematic model (Warfield, 1974;Ojha et al., 2014;Chaple et al., 2018;Malek and Desai, 2019;Zhou et al., 2019;Yang and Lin, 2020). The ISM method allows individuals or groups to develop a diagram showing the relationships between the factors involved in complex situations as a basis for effective decisionmaking (Jhawar and Garg, 2018). ISM suggests involving experts in constructing the contextual relationships through brainstorming techniques or nominal group techniques. The steps for implementing ISM are given as follows:

    • 1. Identifying critical success factors relevant to the problems either by surveys, involving a group of problem-solving discussion techniques, through literature review, or both.

    • 2. Establishing contextual relationships between factors which pairs of features will be examined by applying information from selected respondents.

    • 3. Developing a structural self-interaction matrix (SSIM) that shows the pairwise relationship between factors.

    • 4. Developing SSIM's initial reachability matrix and examining the transitivity matrix of contextual relations based on the fundamental assumption in the ISM, which states how factors are related to each other, to form the final reachability matrix.

    • 5. Partitioning the final reachability matrix into various levels, which include the process of identifying the hierarchical level of each factor based on the reachability set, the antecedent set, and the intersection set so that the level of each factor can be determined.

    • 6. Developing a conical matrix based on the level of each factor. From the matrix, the ranking of each element can be determined, which refers to the total value of the driving power and dependence power.

    • 7. Furthermore, a directed graph (digraph) can be developed based on the relationships given in the final reachability matrix and the conical matrix. By removing the transitivity that occurs, the final ISM model can be obtained.

    • 8. Converts the resulting digraph to an ISM-based model by replacing nodes with statements.

    • 9. Conducting MICMAC analysis to categorize the factors into clusters, including autonomous, dependent, linkage, and independent clusters.

    As this study uses an exploratory approach, then qualitative research methods based on case studies are considered appropriate and suitable. The selected respondents are stakeholders who have important roles in the business and also have experience in their fields. Five business owners of metal manufacturing SMEs were involved in this study. The selected companies are members of the cluster fostered by the Regional Industry Service and are included in the pilot project to accelerate the development and growth of medium-sized industries in East Java, Indonesia. Surveys were done to assess the relationship between elements of related factors, either directly or indirectly. The results of the survey were then combined and further analyzed by the experts in a Focus Group Discussion (FGD) session. The experts consist of two academic practitioners and one government representative from the Regional Industry and Trade Office. Finally, the aggregate interaction matrix was finalized based on the opinions of the experts.

    The factors that become the key drivers are obtained from a literature study. The collected factors were then reviewed through brainstorming to incorporate the expert’s judgments and opinions. The initial stage of this research is to summarize all the elements of the factors as a key strategy that emerged from an in-depth assessment process from all parties involved so that later it will be used as inputs in determining the elements of strategic planning. The results of the literature study and brainstorming bring up eleven factors (see Table 1), which are then used to formulate the ISM model.

    4. RESULT AND DISCUSSION

    4.1 Structural Self-Interaction Matrix (SSIM)

    To collect data related to the relationship between elements, surveys were done on the selected respondents. Each respondent identified the relationship between factor elements based on the direct and indirect relationship. The questionnaire results will then be combined and analyzed further as a basis for developing and achieving the interaction matrix. The SSIM analysis and development process uses the following four symbols, which indicate the direction of the relationship between the factors (i and j) based on the standard ISM methodology (Warfield, 1974) and (Malek and Desai, 2019). The four symbols are:

    • V: Factor i will help reach factor j;

    • A: Factor j will help reach factor i;

    • X: Factor i and j will help each other;

    • O: Factor i and j are not related to each other.

    Table 2 shows the results of the Structural selfinteraction matrix (SSIM). The following are examples of statements explaining the use of the symbols V, A, X, and O.

    • 1. Based on discussions with experts, smart technology (C1) is considered to have a significant relationship in achieving product quality improvement (C3), therefore the relationship between the two factors is rated “V”. Likewise, productivity and profitability (C2) which supports the achievement of competitiveness (C11), is also rated as “V” relation.

    • 2. Collaborative and partnership strategies (C8) are influenced by regulations and government support (C9), therefore their relationship is given an “A” rating. Likewise, the reliability of technology and equipment (C5) whose achievement requires support and commitment from top management (C10), is also given an “A” rating.

    • 3. The quality of the production process (C4) and the labor’s quality and productivity (C6) mutually support each other’s achievements, so this mutual relationship is rated with an “X” rating. Likewise, product quality (C3) and process quality (C4) both are supporting each other, so their relationship is also rated with an “X” rating.

    • 4. Government support and regulation (C9) and strategy for competitiveness (C11) do not affect each other, so the relationship between the two is rated as “O”. Likewise, there is no direct relationship between increased profitability and productivity (C2) and collaborative support (C8), which also be rated as “O”.

    4.2 Reachability Matrix (RM)

    The main step in forming a reachability matrix is to change the SSIM into a binary matrix, which is also called the initial affordability matrix (Table 3). The step was done by translating the symbols V, A, X, and O with values 1 and 0 according to the following relationship criteria:

    • • If entries (i, j) in SSIM are V, entries (i, j) in the range matrix become 1, and entries (j, i) become 0,

    • • If entries (i, j) in SSIM are A, entries (i, j) in the range matrix become 0, and entries (j, i) become 1,

    • • If the entry (i, j) in SSIM is X, entry (i, j) in the affordability matrix becomes 1 and entries (j, i) also become 1, and

    • • If entries (i, j) in SSIM are O, entries (i, j) in the affordability matrix become 0, and entries (j, i) also become 0.

    Once the initial reachability matrix is obtained, the next step is to determine the final reachability matrix, as given by Table 4. The final reachability matrix is generated by deciding the transitivity relationship according to step 4 in the ISM methodology. The values of driving power and dependence power of each factor are shown in Table 4. The driving power (DPWR) is the total number of all factors, that cause or encourage the achievement of these factors. Whereas, the dependence power (DP) shows the total number of factors, that help to achieve each factor. The values of DPWR and DP of each factor are then used in the classification process of MICMAC analysis.

    4.3 Level Partitions

    After the Reachability Matrix is developed, the next step is to determine the level of the partition. Based on the RM, we obtain the reachability and antecedent sets of each factor. The intersection set is derived based on the intersection of the set of all factors. Factors with the same set of reachability and intersection will be placed at the top level in the ISM hierarchy (see Table 5). Once the top level of the hierarchy is obtained, the factor is removed. This step can be continued to get the level of all other factors. These identified levels can later be used to construct the ISM digraph model. The overall levels of the partitions are summarized in Table 6.

    From Table 6, it can be seen that C11 is at the top level of the hierarchy (level I). Subsequently, C2 and C7 are at level II, followed by C3, C4, C5, and C6 at level III, C1 at level IV, C8 at level V, and C10 and C9 are at levels VI and VII respectively and become key elements.

    4.4 Analysis of Conical Matrix (CM)

    Factors that are at the same level are then grouped, and a Conical Matrix (CM) is formed. The conical matrix gives the total value of the Driving Power and Dependence Power of each element. The total value of these two forces can help in the preparation of the MICMAC matrix in the classification process. The conical matrix is presented in Table 7.

    4.5 Construction of the Digraph Model

    Based on the final reachability matrix, a structural model is then developed into a digraph model, as shown in Figure 1.

    The digraph is constructed after the indirect links are removed. It becomes the final ISM model, once the transitivity relationships are removed and replacing the node with a statement of the driving factors, as in Figure 2.

    From the digraph model, it can be seen that government regulations and supports (C9) and top management support and commitment (C10) are the main driving factors, hence are positioned at the bottom of the hierarchical level of the ISM model (see Figure 2). The strategy for competitiveness (C11) is a key factor identified as the top-level element in the ISM model.

    4.6 MICMAC Analysis

    Matriced'Impacts Croises-Multiplication Appliqué A Classement (MICMAC) which is also called by Cross- Impact Matrix Multiplication Applied to Classification, is used to explore the power of interrelations and dependence between elements and to classify them into several dominant clusters (Digalwar et al., 2015). This analysis is also done to identify the key factors that can drive the system in various categories (Jhawar and Garg, 2018). From the calculation of dependence power (DP) and driving power (DPWR) in the reachability matrix, the factor elements can be classified into four clusters. The coordinates of DP and DPWR for each element factor are summarized in Table 8.

    Based on the coordinates of each factor (see Table 8), the factors can be then categorized into four clusters, which are:

    • 1. Autonomous cluster: elements belonging to this cluster have weak driving force and dependence. They are relatively disconnected from the system with very few links. From the MICMAC analysis, no elements were classified as autonomous clusters. This shows that all drivers are considered capable of creating sustainable manufacturing values. Therefore, the management must pay attention to the overall characteristics of the driving factors for the success of achieving sustainable competitiveness.

    • 2. Cluster dependent: this category includes elements that have weak driving forces yet strong dependencies. From the MICMAC analysis, productivity and profitability (C2) and promotion and company's reputation (C7) are included in this category, as well as a strategy for competitiveness (C11). The achievements of those belonging to this category are highly dependent and influenced by many factors, therefore the management must prioritize dealing with these driving factors.

    • 3. Linkage cluster: this cluster includes elements that have strong driving forces and strong dependencies. Factors belonging to this cluster tend to be unstable. Any changes made to the elements will have an impact on the other elements. In addition, it will also provide feedback to themself. Product and service quality development (C3), quality of the production process (C4), reliability of production equipment and technology (C5), and labor’s quality and productivity (C6) are included in this category. Due to its instability, the existence of a centralized control process is crucial so that the overall linkage of the driving elements in this cluster can be operated optimally (Jain and Banwet, 2013).

    • 4. Independent cluster: elements included in this cluster have strong driving forces, but weak dependencies. This element is the most important driver that influences and has a crucial impact on other elements at the top of the hierarchy (Zhou et al., 2019). These driver elements are also key success or key performance factors. From the MICMAC analysis, it is known that government regulation and support (C9), management support and commitment (C10), collaboration and partnership (C8), and the application of smart technology (C1) are the main key factors in implementing technological innovation. All of these factors have vital roles in efforts to implement and improve product quality (C3), process (C4), reliability of technology (C5), and also the quality of employees (C6) in achieving sustainable competitiveness. The management must pay more attention to the four factors classified in this cluster, considering that all of the four factors have a very strong driving force.

    The driving and dependent powers coordinate diagram and the results of the MICMAC clustering are shown in Figure 3.

    As shown in Figure 4, from the MICMAC analysis it is understood that there is no factor included in the autonomous cluster. This suggests that the overall factors are essential to influence the company’s performance in achieving sustainable manufacturing value creation. As decision-makers, companies must pay attention to the general factor-driven to become successful in the application of technological innovation. Based on the digraph model, the eleven factors are grouped into seven levels. Top management support and commitment (C10) and government regulations and supports (C9) are at levels 6 and 7. They both have critical and significant roles in creating manufacturing value through the application of technological innovation.

    In this case, the management's commitment and supports to improve their performance through the application of new technology is the main thing needed to initiate change. These findings support research conducted by Orji (2019), which emphasized that management commitment is one of the keys to organizational change towards sustainability. More straightforward, facilitative, efficient, and practical government regulations support and encourage management commitment to change. Government regulations that favor the sustainability of SMEs, such as workforce regulations, tax policies, ease of capital loans, and cooperation, are needed to make it easier for the industry to improve and develop itself towards global competitiveness. As stated by Malek and Desai (2019), government regulations that support the environment can strictly encourage the manufacturing industry's achievement towards sustainability. Government support has a vital role in setting rules and processes for implementing sustainability innovations in the manufacturing industry (Khurana et al., Haleem and Mannan, 2019).

    The collaboration and partnership (C8) and application of smart technology (C1), which are at levels 5 and 4, also become the main drivers in the application of new technology in manufacturing SMEs. The existence of these two factors requires direct support and encouragement from factors from the previous level, which are the government regulations and supports (C9) and management support and commitment (C10). This finding is also supported by the MICMAC results, which show that the four main factors included in the independent cluster play an essential role in the application of technological innovation in the manufacturing industry. Previous studies found that government support is crucial for manufacturing companies to develop human resources, market support and readiness, environmental benefits, product quality in the form of national standards and IPR, health, and security concerns (Dwivedi et al., 2017). Likewise, commitment and support from top management affect the implementation of smart technology and motivate employees to be able to adapt to the new technologies fast (Khurana et al., 2019). On the other hand, the implementation and application of smart technology cannot be separated from the support of collaboration and partnership with all stakeholders involved. Mutually beneficial collaboration enables the development of innovation and transfer of technological knowledge, which is the key to successful sustainable value creation (Tiengtavaj et al., 2017).

    The implementation and development of smart technology (C1), which is at level 4, is a key factor that helps the implementation of level-3 drivers. This finding also supports Liu et al. (2020), which stated that the use of smart technology in manufacturing activities, by involving big data analysis and information technology, can assist management in making improvements in the production area. The application of smart technology can improve the four driver factors at level 3, namely: product and services quality (C3), quality of production process (C4), technology and equipment reliability (C5), and labor quality and productivity (C6). Based on the MIC-MAC analysis, these four factors are included in the linkage cluster with the same driving and dependence powers, so they need to get the full attention from the management. Overall, these four key factors are the basis of value creation that can increase productivity (C2) and company reputation (C7), which is at level 2. Factors that are classified in the linkage cluster are all related to competitiveness, therefore improvements made to level 3 lead to a strategy to achieve competitiveness (C11) at level 1. Improvements in the company’s productivity and profitability (C2) strengthen the company's reputation, which is the main driver for creating competitive advantages towards sustainable manufacturing.

    5. CONCLUSION AND FURTHER RESEARCH

    This study has discussed the identification of factors that drive the success of technological innovation in SMEs manufacturing towards sustainable value creation. Based on a literature study and FGD with experts, there were 11 drivers identified. The results of this study found that the management commitment factor is indispensable to support the application of technological innovation in every manufacturing activity. In addition, government support and regulations, collaboration and partnership with stakeholders, as well as the application of smart technology in manufacturing activities, are identified as the basic level in the hierarchy and are the most important factors. These drivers are a key priority for management in increasing manufacturing value creation towards sustainability. Furthermore, the quality of products and services, the quality of the production process, the reliability of technology, as well as the quality and productivity of labor are included in the unstable factors. However, a centralized operational control process, supported by the application of smart technology, can help to control the instability of these drivers. The results of clustering with MICMAC analysis assist managers in designing and formulating the right strategy, based on the more prioritized cluster.

    The ISM model was developed to study the interrelationships and dependencies between drivers to formulate strategies for developing and implementing new technologies. The ISM model helps in identifying the key factors associated with the application of technological innovations that can support sustainable value creation in manufacturing SMEs. This model is also able to integrate all factors based on the perception of experts, however, the results are still not statistically validated. Therefore, future research can examine the problem more deeply and involve other statistical-based methods that can validate the reliability of the model.

    ACKNOWLEDGMENTS

    This research is supported by the Department of Industrial Engineering, Institute Technology Adhi Tama Surabaya; Department of Mechanical Engineering and Department of Industrial Engineering, Brawijaya University, Indonesia.

    Figure

    IEMS-21-1-43_F1.gif

    The initial digraph model of technological innovation and sustainable value creation.

    IEMS-21-1-43_F2.gif

    The final digraph model of technological innovation and sustainable value creation.

    IEMS-21-1-43_F3.gif

    Driving Power and Dependence Power Diagram for MICMAC Analysis

    Table

    Description of the factors driving the application of technological innovation

    Initial Structural Self-Interaction Matrix (Initial SSIM)

    Initial reachability matrix

    Final reachability matrix

    Iteration 1 levels of the partition of reachability matrix

    Levels of elements driving the implementation of technology innovation

    Conical matrix

    Coordinates for Each Factor Based on Its Driving and Dependence Powers in the RM

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