Jurnal Kejuruteraan 33(1) 2021: 73-82https://doi.org/10.17576/jkukm-2020-33(1)-08

Enhancing Green Product Competitiveness through Proactive Capabilities of Manufacturing Firms

Norhuda Salim, Mohd Nizam Ab Rahman* & Dzuraidah Abd. WahabDepartment of Mechanical and Manufacturing Engineering, Universiti Kebangsaan Malaysia, Malaysia

*Corresponding author: mnizam@ukm.edu.my

Received 09 January 2020, Received in revised form 05 May 2020Accepted 15 September 2020, Available online 28 February 2021

ABSTRACT

The development of innovative green products that can compete in unpredictable markets is a challenge for manufacturing firms. Hence, firms need to reconfigure their strategies by considering and strengthening the appropriate set of internal capabilities to achieve effective green product development. Therefore the initiatives implemented by firms in relation to their green practices and green innovation are attracting increasing scholarly attention. However, thus far only a few studies have explicitly explored the relationship between the proactive capabilities of manufacturing firms in the context of green product innovation. Thus, this study investigates the environmental proactivity (EP) and strategic flexibility (SF) of firms, and how these proactive capabilities affect green product innovation (GPI). In addition, it explores the role of GPI in enhancing product competitive advantage (PCA). The data for this study was obtained from 157 respondents representing manufacturing firms in Malaysia. Structural equation modelling was used for the statistical analysis. The results showed that SF partially mediates the relationship between EP and GPI, which indicates that SF facilitates GPI implementation. The results also revealed that GPI acts as a full mediator between SF and PCA, proving that GPI is the crucial factor that can translate SF into competitive advantage. Thus, based on the dynamic capabilities perspective, this study offers insights into the importance of flexibility and innovation in the context of green manufacturing as a means to enhance green product features and remain competitive in the market.

Keywords: Environmental proactivity; Strategic flexibility; Green product innovation; Product competitive advantage

INTRODUCTION

It is widely understood that one of the key challenges facing manufacturing firms today is the need to find the right way to achieve a sustainable competitive advantage in a dynamic marketplace. Therefore, the ability to offer higher value to customers, either through lower prices or by providing more benefits that justify higher prices has become a crucial matter for firms (Saranga et al. 2018). For that reason, it is necessary to proactively and wisely manage a variety of resources on a continuous basis in order to win business (Kuncoro and Suriani 2017). Due to the level of product competition in the market, firms are now taking the initiative to consider the environmental elements of their manufacturing processes and their products’ features (Wan Mahmood et al. 2013). Hence there has been an increase in the implementation of green product development initiatives (Dangelico et al. 2017) to reduce environmental impact and to increase the protection of the ecosystem (Abdullah et al. 2017). The promotion of green campaigns is also gaining momentum as a means of raising awareness about environmental issues among consumers (Tih et al. 2016). The consumer perception of environmentally friendly products is improving (Hojnik and Ruzzier 2016) because some green products indicate important attributes

such as ‘economy’, ‘new technologies’, ‘durability’, ‘easy maintenance’ etc. (de Medeiros and Ribeiro 2017) and are believed to be healthier and safer (Matus et al. 2012).

However, green product development has been a challenge for most firms, especially small-scale firms with limited knowledge and resources (Jamian et al. 2014). Moreover, some firms consider green product innovation (GPI) as a non-significant investment (Gabler et al. 2015). Further, the implementation of GPI is also difficult to understand and its impact on the firm is unclear (Fernando and Wah 2017). Therefore, it is crucial for firms to identify an appropriate green innovation strategy in order to continuously improve their products while minimizing environmental impact and achieving better business performance (Abu Seman et al. 2019; Fernando and Wah 2017).

To address this problem, previous scholars have come up with various factors and strategies for implementing GPI that can be adopted by manufacturing firms, as shown in Table 1.

From Table 1 it can be seen that several firm attributes influence the implementation of GPI. These factors include green image, green creativity, internal collaboration and absorptive capacities (Albort-Morant et al. 2018; Amores-Salvadó et al. 2014; Chen et al. 2016; Kong et al. 2016;

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Xie et al. 2019). However, as regards the effect of firm initiatives that centre on proactive/dynamic capabilities, the findings are very limited. Chen et al. (2016)and examines the mediating effect of green creativity. Structural equation modeling (SEM studied proactive innovation capability from a general perspective, while Dangelico et al. (2017) only considered proactive capabilities from a resource integration standpoint. Therefore the study of proactive capabilities needs to be expanded as these capabilities cover a broad range of contexts. The need to study GPI implementation with a focus on proactive capabilities is in line with the recommendations of Amui et al. (2017) and Salim et al. (2019), who highlighted the importance of proactive/dynamic capabilities to the firm in developing the requisite knowledge and skills for improving product features and engaging in sustainability-oriented change.

In light of the above, this study considers two dimensions of proactive capability, namely environmental proactivity (EP) and strategic flexibility (SF) with the aim of (1) investigating the effect of EP on GPI; (2) examining the role of SF as a mediator between EP and GPI; (3) investigating the effect of SF on product competitive advantage (PCA); and (4) examining the role of GPI as a mediator between SF and PCA. To achieve these aims, this study analysed data obtained from 157 manufacturing firms listed as exporters in the Malaysian External Trade Development Corporation (MATRADE) database.

Drawing on the dynamic capabilities view (DCV), this study conceptualizes a framework in which EP is connected to GPI and in which SF is linked to PCA. According to the DCV, firms respond promptly to dynamic changes in the environment and implement strategic decisions to ensure the right direction, thus achieving better performance (Teece et al. 1997). Dynamic capabilities form the basis for the development of competency that is rare, non-substitutable

and difficult for competitors to replicate (Hunt and Morgan, 1996). Hence these capabilities are indispensable in GPI, which in turn enhances PCA.

EP AND GPI

Environmental proactivity refers to the voluntary action taken by a firm to attempt to reduce or eliminate the negative effects of its product activity on the natural environment (Menguc and Ozanne, 2005). A previous study showed that EP gives a direct positive effect on operational and financial performance (Sambasivan et al. 2013). Similarly, Primc and Čater (2016) found that EP helps firms to survive in a long run and positively impact their competitive advantage. However, Junquera and Barba-Sánchez (2018) concluded that an EP strategy has no direct impact on financial performance. It may be that the findings regarding the effects of proactivity capabilities are inconsistent due to differences in the levels of dynamic capabilities possessed by firms, where firms with higher levels of dynamic capabilities reach greater achievement compared to less dynamic firms (Primc and Čater 2016). In the innovation context, Dai et al. (2017) demonstrated that EP encourages firms to engage in green collaboration, thus enhances green innovation performance. Green firms need to be proactive in taking full responsibility for the entire lifecycle of their products, from the raw materials selection to the final disposal of the products or parts (Abdullah et al. 2017). In addition, EP helps firms to be more sensitive to the demands of society and improve their environmental practices, which in turn leads to better product designs that open up new market opportunities (Junquera and Barba-Sánchez, 2018). For that reason, this study formulated the following hypothesis:

H1: EP has a positive and significant effect on GPI.

TABLE 1. Previous Research Findings on How to Improve Green Product Innovation

No Reference Result Firm attribute Firm initiative1 Amores-Salvadó et al.

(2014)Green corporate image affects the performance of environmental product innovation.

/

2 Chang (2016) Corporate environmental commitment influences green product innovation.

/ /

3 Chen et al. (2016) Proactive green innovation and green creativity positively impact green product development.

/ /

4 Kong et al. (2016) Internal environmental collaboration helps improve green product innovation.

/

5 Dangelico et al. (2017) Sustainability-oriented dynamic capability affects eco-design capability, while external resource integration affects green innovation capability.

/

6 Albort-Morant et al. (2018)

Potential and realized absorptive capacities can improve green product innovation.

/

7 Ma et al. (2018) The novelty-centred and the efficiency-centred business models impact green product innovation.

/

8 Chen and Liu (2019) Customer participation promotes green product innovation. /9 Xie et al. (2019) The green image of a firm affects its green product innovation and

financial performance./

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SF MEDIATES EP AND GPI

Strategic flexibility is the ability of the firm to respond promptly to changing demands that then allow the firm to remain competitive (Hitt et al. 1998). Fundamentally, SF implicates the ability to address short-term fluctuations in demand, raw material shortages, or equipment failures (Carlsson 1989) and to reduce the complexity involved in producing a variety of products (Suarez et al. 1995). In addition, SF enables firms to easily adapt to a changing environment and to seize new market opportunities as well as to deliver successful new products (Kandemir and Acur 2012). Perez-Valls et al. (2016) showed that SF is a key factor that links green practices to organizational performance. Likewise, Xiu et al. (2017) found evidence to show that organizations that focus heavily on SF are more likely to adopt innovative human resource practices. In line with this, firms that practise EP can improve the quality, delivery and flexibility of operations to meet customer requirements (Dai et al. 2017). In general, firms that take a dynamic approach are more likely to implement flexible working regimes that could provide opportunities for innovation (Ramanathan et al. 2017). The above directed to the development of the following hypotheses for this study:

H2: EP has a positive and significant effect on SF.H3: SF has a positive and significant effect on GPI.H3a: SF mediates the relationship between EP and GPI.

SF AND PCA

According to the DCV, SF encourages firms to better reallocate resources and reconfigure existing operations to ensure that the needed resources are constantly available and that the firm can continue to be competitive despite any uncertainty in the external environment (Zhou and Wu 2010). In terms of production, process flexibility involves the production of multiple products in the same plant or on the same production line (Beraha et al. 2018). In this regard, firms with higher flexibility can quickly change their product development decisions to respond to market changes or technological changes and thus transform these

changes into opportunities that they can leverage to their advantage (Kandemir and Acur 2012; Mustaffa et al. 2018). A flexible system ensures that a variety of products can be produced from new combinations of standard components (Beraha et al. 2018), besides reducing production time, lowering production costs and widening product ranges (Chan et al. 2017). Hence this study proposed the following hypothesis:

H4: SF has a positive and significant effect on PCA

GPI MEDIATES SF AND PCA

Strategic flexibility allows firms to fully utilize their resources for wider and more advanced purposes such as green design, green manufacturing, green marketing, etc. (Yang et al. 2015). This suggests that green practices and SF are intertwined and can thus strengthen a firm’s ability to manage and utilize resources to reach environmental goals (Perez-Valls et al. 2016).

This view is supported by Xiu et al. (2017) and Beraha et al. (2018), who found that organizations that are more focused on SF are more likely to embrace innovative practices.

In addition, firms that adopt SF can easily adapt their use of resources to new applications and increase their usage in GPI (Yang et al. 2015). The aims of engaging in GPI include developing new products ahead of competitors, lowering costs, seizing opportunities and gaining a lead in the market (Chen et al. 2016). Previous research by Wong (2012) on electronics firms in China indicated that GPI has a positive effect on PCA. However, Dangelico et al. (2017) state that larger firms are more capable of turning GPI into firm success. Therefore, it is essential to further examine the role of GPI in the relationship between SF and PCA in the context of a wider sub-sector, by testing the following hypotheses:

H5: GPI has a positive and significant effect on PCA.H5a: GPI mediates the relationship between SF and

PCA.Figure 1 depicts the conceptual and hypothesis model

developed for this study.

FIGURE 1. Conceptual framework of proactive capabilities for GPI and PCA

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METHODOLOGY

DATA COLLECTION AND SAMPLE

A sampling questionnaire survey was sent to manufacturing firms in Malaysia listed as exporters by the Malaysia External Trade Development Corporation (MATRADE). These firms comply with international standards including product sustainability requirements. A total of 500 firms were randomly selected from the MATRADE database (http://www.matrade.gov.my) and the questionnaire surveys were directed by email to the middle- or upper-level managers of the selected firms. Data collection was conducted between November 2018 and March 2019. To increase the response rate, follow-up telephone calls were made two weeks after sending the email. At the end of this process, 161 questionnaires were collected, representing a response rate of 32.2%. After eliminating the invalid questionnaires, 157 usable questionnaires remained in the sample. This number was deemed sufficient to proceed with the analysis of the four constructs in the developed model (Hair et al. 2010).

MODEL AND MEASUREMENT

Structural equation modelling (SEM) was employed for the data analysis, which was done using IBM-SPSS-AMOS software (version 21.0). This type of analysis methodology was selected because it is the most efficient method for validating latent constructs in a structural model, besides its ability to simultaneously assess and organize a series of interrelationships among the latent constructs in a model (Awang 2015). The survey questions were designed to be answered on a five-point scale ranging from 1 = “strongly disagree” to 5 = “strongly agree”. The study adapted and customized items from prior research and the questionnaire was then validated by academic experts. Explanatory factor analysis was performed on all the items in the survey and any items that exhibited weak factor loadings (below 0.6) (Hair et al. 2010) were deleted.

This resulted in the removal of three items. The remaining 38 out of 41 items were then subjected to further analysis. To ensure the sampling adequacy, this study measure Kaiser-Meyer-Olkin (ranged from 0.832 to 0.873) and employed Bartlett’s test of sphericity. The reliability of the constructs was tested by determining the value of the Cronbach’s alpha coefficient. The values for the constructs

ranged from 0.732 to 0.810, thus exceeding the 0.7 threshold and specifying that the model had acceptable reliability (Hair Jr et al. 2006).

RESULTS AND DISCUSSION

DEMOGRAPHIC PROFILE

Profile analysis showed that 72% of the respondents were assistant managers, assistant directors or above (refer Figure 2). This indicated that quality data would be obtained by this study because this group of respondents is deemed to be more knowledgeable about their organization’s strategic orientation (Campbell, 1955). In relation to the industrial subsectors covered by the survey data, most of the respondents (65%) represented the electrical and electronics, chemical, and mechanical and equipment industries, all of which are the main subsectors driving the growth of the manufacturing sector in Malaysia.

MEASUREMENT MODEL

Four constructs, nine sub-constructs and 38 items formed the measurement model. Confirmatory factor analysis was executed to measure the model for the unidimensionality, validity and reliability of the constructs. All the factor loadings as shown in Appendix A were greater than the minimum threshold of 0.6, except for SCF2 which had a value of 0.582, hence this item was deleted. Subsequently, construct validity, convergent validity and discriminant validity were checked to ensure the significance of the measurements. Initially however, the construct validity analysis produced an unacceptable goodness of fit value, which specified that the model had an overall poor fit. Therefore, the modified index measurement value was checked, and it was found that the covariance between PS4 and PS5 was 22.575 (>15.0) and that this was affecting the fitness indices. Therefore, both of these items were set as ‘free parameter estimates’, which ultimately led to an improvement in the fitness index so that acceptable values were obtained (refer to Table 2).

The average variance extracted (AVE) was also inspected and it was found that all the AVE values exceeded the threshold of 0.5, indicating that all the items were significantly loaded onto their construct, as shown in Table

FIGURE 2. Profile Analysis

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3. The composite reliability values also surpassed theminimum threshold level of 0.70 (Hair et al. 2010), whichmeant that the constructs could be considered to be reliableand consistent.

In addition, Table 3 shows that the diagonal value (representing the square root of AVE) for each construct was higher than the values in the rows and the columns, which proved that the discriminant validity for all constructs was attained. Further, the construct correlation values were below 0.85, specifying that this model had no multicollinearity problems.

SEM - STANDARDIZED PATH COEFFICIENTS

After the measurement model was proven valid and reliable, SEM was run to examine the hypotheses. Figure 3 depicts the standardized path coefficients of the model. At this stage, the coefficient of determination (R2) was assessed. Referring to Figure 3, the value of R2 for PCA was 0.73. This value implies that 73% of the PCA performance could be estimated by using GPI and SF in the model. In other words, the contribution of GPI and SF in estimating PCA was 73%.

In addition, EP and SF contributed 72% of the variance in estimating GPI, indicating good support for the developed model. Besides, the fitness index has also surpassed the minimum value and the factor loading for all items has exceeded 0.6. Furthermore, the value of standardized residual covariance was also observed, and was found to have an absolute value of less than 2, indicating that the structural model was correctly specified.

HYPOTHESIS TESTING

The statistical results support H1, H2, H3, and H5, but unexpectedly reject H4 (refer to Table 4). The results indicated that EP had a significant positive effect on GPI

(β = 0.420, p < 0.001), hence H1 was accepted. Indeed, EP and GPI are closely related in the pursuit of green activity. Concerning H2, EP showed a significant positive effect on SF (β = 0.515, p < 0.001), consequently H2 was supported. Firms that truly care about the environment will consider flexible work processes to meet their diverse needs. Accordingly, the results also supported H3, where SF showed a positive significant effect on GPI (β = 0.614, p < 0.001). This means that flexibility in the supply chain, resources, and functionality facilitates product innovation.

However, H4 which predicted that SF would have a positive and significant effect on PCA, was not supported. Even though the direct link between SF and PCA showed a positive effect, it was insignificant (β = 0.017, p > 0.1), therefore H4 was rejected. Although SF is considered to be a dynamic capability that drives the firm’s competitive advantage (Santos-Vijande et al. 2012), the direct effect of SF on PCA was not proven by this study. Finally, the results revealed that GPI exerted a significant positive effect on PCA (β = 0.760, p < 0.001), as postulated by H5. This indicates that GPI can increase the competitiveness of a product in the market.

MEDIATION ANALYSIS

This study applied the method in Baron and Kenny (1986) to test the mediator effects in the model. Referring to Figure 3, the direct effect between EP and GPI was significant with a value of 0.39. As for the indirect effect, which involved the link between EP → SF and SF → GPI, this was also significant with a coefficient value of 0.29 (0.50 x 0.58). This result indicated that SF acts as a partial mediator in the relationship between EP and GPI, hence H3a was supported. As regards the direct effect between SF and PCA, this effect was not significant. However, the indirect effect, which involved the link between SF→ GPI and GPI → PCA, was significant with a coefficient value of 0.49 (0.58 x 0.84),

TABLE 2. Fitness Indices of the Measurement Model

Category Index Initial Index Value

Final Index Value Comments Level of

Acceptance1. Absolute fit RMSEA (Root-Mean-Square

of Error Approximation)0.044 0.041 Achieves the required level RMSEA < 0.08

GFI (Goodness of Fit Index) 0.789 0.806 Achieves the required level GFI > 0.82. Incremental fit CFI (Comparative Fit Index) 0.946 0.953 Achieves the required level CFI > 0.9

TLI (Tucker-Lewis Index) 0.941 0.949 Achieves the required level TLI > 0.93. Parsimonious fit Chisq/df (Chi-square/Degree

of Freedom)1.290 1.251 Achieves the required level Chi-square/df < 3

TABLE 3. Discriminant Validity Index Summary for the Constructs

CR AVE MSV PCA EP SF GPI

PCA 0.824 0.702 0.681 0.838

EP 0.856 0.749 0.411 0.641 0.865

SF 0.831 0.622 0.587 0.679 0.496 0.788

GPI 0.832 0.713 0.681 0.825 0.635 0.766 0.844

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thus GPI acts as a full mediator in the relationship between SF and GP and H5a can be supported.

THE RELATIONSHIP BETWEEN EP AND GPI

The finding of this study that EP affects GPI is in line with the findings put forward by previous researchers, who link EP to innovation (Garcés-Ayerbe et al. 2016; Garcés-Ayerbe and Cañón-de-Francia, 2017; Ryszko 2016). Primc and Čater (2016) emphasized that firms with higher levels of EP tend to innovate due to a higher degree of risk-taking behaviour. On this point, EP supports decision-making in the eco-design process and the selection of methods for eco-product development (Mahmood et al. 2018). In addition, EP facilitates the forecasting of future norms and social trends, thereby enabling firms to embrace green creativity to improve their green product development performance (Chen et al. 2016). In other words, EP can stimulate a more responsive GPI implementation.

THE MEDIATING IMPACT OF SF

This study found that SF partially mediates the relationship between EP and GPI is in line with the results reported by

Chen et al. (2017), who found that SF partially mediates the effect of IT support on firm performance because SF drives firms to customize and optimize the use of their resources to improve their strategic plans. Firms with greater flexibility are able to quickly modify their product specification to react to the changing market environment and technological opportunities. Moreover, firms in emerging economies desperately need SF because they are more likely to experience serious resource scarcity and high contextual uncertainty (Li et al. 2017). However, having said that, in the context of green manufacturing, SF is crucial for translating the benefits of EP practices and for improving GPI.

THE RELATIONSHIP BETWEEN SF AND PCA

The results of this study showed that SF does not have a significant effect on PCA. Similarly, Kortmann et al. (2014) found that SF does not directly affect operational efficiency, but requires the presence of innovative ambidextrous capabilities in order to tie both of these factors together. In this regard, Ahmadi and Mohd. Osman (2018) argued that SF alone is not enough to achieve sustainable competitive advantage. Basically, SF requires that new information and knowledge is assimilated and configured in such a way so as

FIGURE 3. Standardized path coefficients

TABLE 4. Regression Path Coefficients and Their Significance

Hypothesis Est. β SE CR P ResultH1 Green Product Innovation_GPI ← Environmental Proactivity_EP .420 .118 3.558 *** SignificantH2 Strategic Flexibility_SF ← EnvironmentalProactivity_EP .515 .122 4.214 *** SignificantH3 Green Product Innovation_GPI ← Strategic Flexibility_SF .614 .125 4.914 *** SignificantH4 Product Competitive

Advantage_PCA← Strategic Flexibility_SF .017 .164 .103 .218 Not Significant

H5 Product Competitive Advantage_PCA

← Green Product Innovation_GPI .760 .187 4.058 *** Significant

Note: ***p < 0.001

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to benefit the firm (Fernández-Pérez et al. 2012). Therefore, SF needs the assistance of other relevant factors to create and support a more focused and integrated strategy to ensure product competitiveness.

THE MEDIATING IMPACT OF GPI

In this study, GPI was proven to be a full mediator linking SF and PCA. Prior literature suggested that balancing the trade-off between GPI and PCA is important in developing a competitive business strategy. Enhancing the functionality of products through innovative green design may increase their value through, for example, the usage of recyclable, non-toxic and less-polluting materials, and may also reduce the negative environmental impacts of a product’s total life cycle (Chen et al. 2016; Suhariyanto et al. 2018). In a dynamic market, firms must strive to produce outstanding products to gain competitive advantage in order to generate value for the firms. Therefore, the role of GPI as a full mediator in the relationship between SF and PCA is of great importance.

CONCLUSION

The findings of this study enrich the literature on the efforts of firms to improve GPI in order to achieve product competitiveness through the investigation of the role of proactive capabilities according to the DCV. Specifically, the empirical results of this study suggest that SF is an important factor that can boost GPI. Moreover, GPI

seems to be a crucial factor that may be able to translate green practices into competitive advantage. The identified correlation between EP and GPI may encourage firms to pursue environmental strategies in order to achieve high level of competitiveness. Manufacturing firms need to be able to align and strengthen their internal capabilities to innovate continuously and faster than their rivals. In this regard, top management need to be proactive in changing, developing, and restructuring their internal strategy over the long term to ensure that their firms are and continue to be strategically flexible in producing an extensive range of products to stay competitive.

In closing, some limitations of this study should be noted. First, this study does not distinguish the effects by the size or level of the firm. Therefore, the differentiation of firms into small and medium-sized enterprises or multinational corporations, for example, would generate more detailed results and facilitate comparisons between groups. Second, this work focuses on manufacturing firms listed as exporters in Malaysia with a small number of respondents, which limits generalizability of the findings. Therefore, future research may gather much larger data sets and may involve sample from multiple countries.

ACKNOWLEDGEMENT

The authors would like to thank the Universiti Kebangsaan Malaysia for its support through the research grant FRGS/1/2018/TK08/UKM/02/1 and the Department of Civil Service, Malaysia for the scholarship of the researcher.

APPENDIX A

Indicator Factorial loadEPSources: Sambasivan et al. (2013), Sen et al. (2015)Environmental Initiative (EI)EI1) Create recycling initiatives .715EI2) Implement environmental criteria in supplier selection .681EI3) Identify new environmental aspects in terms of their impact .753EI4) Regular volunteering of information about environmental management to stakeholders/customers/communities .784Environmental Commitment (EC)EC1) Top management support for environmental training .779EC2) Actively comply with environmental regulations .801EC3) Has clear objectives and long-term environmental plans .783SFSources: Duclos et al.(2003), Oke (2013), Stevenson and Spring (2007), Zhang et al. (2003)Supply Chain Flexibility (SCF)SCF1) Well-developed supplier selection systems .730SCF2) Flexibility terms are included in the supply contracts .582 (deleted)SCF3) Efficiently handle emerging customer demands in terms of product changes or customer location changes .765SCF4) Find alternative sourcing partners to meet customer needs .867SCF5) Align labour skills in supply chain activities .811

cont.

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DECLARATION OF COMPETING INTEREST

None.

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