International Journal of Entrepreneurship (Print ISSN: 1099-9264; Online ISSN: 1939-4675)

Research Article: 2021 Vol: 25 Issue: 5S

Development of Business Performance under Environmental Uncertainty: Lesson from Thailand's Eastern Economic Corridor

Supamit Srisawat, Suan Sunandha Rajabhat University

Wissawa Aunyawong, Suan Sunandha Rajabhat University

Abstract

 At present, Thai government has a policy of special development zones in the eastern Thailand to increase the country's competitiveness. Therefore, this research aims to examine the moderating role of environmental uncertainty on the effect of logistics and supply chain flexibility on performance of businesses in the Eastern Economic Corridor (EEC). The research was a quantitative study using questionnaires as a research instrument. Data were collected from 360 companies in the EEC obtained by stratified sampling. The statistics used in the data analysis were the structural equation model (SEM). The results showed that logistics and supply chain flexibilities had a positive effect on business performance with the mediation effect of environmental uncertainty. In addition, this research suggests that educational institutions, the companies and related departments should jointly provide training courses on how to improve logistics and supply chain flexibilities under the uncertainty of the current business environment.

Keywords

Logistics Flexibility, Supply Chain Flexibility, Environmental Uncertainty, Business Performance

Introduction

Currently, environmental uncertainty can contribute to business performance. The environmental uncertainty makes firms adopt greater innovativeness and thus perform better. Under the endless changing and uncertain market circumstances, successful companies are likely to develop groundbreaking strategies to satisfy consumer demands and to launch new product (Mee-ngoen et al., 2020). However, managers are difficult to make a decision on growth strategy (Kafetzopoulos et al., 2020). Thailand, therefore, has a small economic growth as well as the population income is classified as upper middle income based on the World Bank's criteria with a Gross National Income (GNI) per capita of USD 4,125 but not exceeding USD 12,736. According to the latest data in 2020, the country has an average income per capita per year of USD 5,720 (National Statistical Office, 2020). To develop country’s competitiveness for moving from middle-income to high-income country, the country must create value for goods and services from the end-to-end to support a wide range of general and specific needs (Goldberg, 2019).

As a result, the government has a policy to create added value and enhance business performance by requiring integrated work for various departments to drive development in the same direction and achieve the vision of the country in terms of stability, wealthy and Sustainability. This will be beneficial to increasing the country's competitiveness and upgrading the quality of life of people in the area, which is in line with the principles of good governance by designating the area of 3 provinces, comprising Chachoengsao, Chonburi and Rayong, to be developed into the Eastern Special Development Zone or Eastern Economic Corridor (EEC) in a concrete manner as soon as possible to be the mainstay of the country's developments of economy and technologies (The Eastern Special Development Zone Policy Office, 2021).

In addition, due to globalization that has changed countries, societies and communities at each level rapidly because the world is connected through the process of free trade, industrial investment, tourism and communication via various media, including the movement of labor and capital which often arises from development in the infrastructure of that country or society (Phrapratanporn et al. 2019; Dabic et al., 2020). When businesses in Chachoengsao, Chonburi and Rayong provinces cannot avoid from the effects of globalization, they need to compete with temporarily or long-term foreign businesses (Soonthornpipit et al., 2021). If they have no adjustment to create logistics and supply Chain Flexibilities, they cannot survive in such highly competitive situations (Aziz et al., 2017; Yu et al., 2018; Aunyawong et al., 2020; Ko et al., 2018; Irfan et al., 2019; et al. Maqueira et al., 2020). Any changes in technology, competition, demand and supply are business environment uncertainty that is happening in those provinces, which will definitely impact on performance of businesses in the EEC. Therefore, all parties must be aware of and focus on such changes (Hong, Lee & Zhang, 2018; Chai, et al., 2019; Jilke, 2020). For that reason, the study, aims to examine the moderating role of environmental uncertainty on the effect of logistics and supply chain flexibility on performance of businesses in the EEC, in line with the government's policies and the current situation to achieve the goal of increasing the country's competitiveness.

Literature Review

Business Performance

Business Performance (BP) refers to results or outcomes that are indicators of success. Such outcomes include productivity, profit, service quality, customer or employee satisfaction and quality of work life to build employee engagement with the organization (Lee et al., 2015; Chienwattanasook et al., 2019; Jermsittiparsert, 2021). BP is divided into 3 dimensions: Financial Achievement, Operational Excellence, and Marketing Performance, as revealed by Simon, et al., (2015); Al Issa (2020); Kurniawan, et al., (2020); Ilmudeen, et al., (2019).

Logistics Flexibility

Tiwari (2015) define Logistics Flexibility (LF) as the supply chain's ability to ensure that there is no disruption to the supply from supplier to end-consumer under uncertain and unstable environments by minimizing the variance between supply and demand without any damages or problems to the supply chain resources and the costs of that supply chain to maintain profitability and responsiveness (Aunyawong et al., 2020). LF was divided into four dimensions: physical supply flexibility, purchasing flexibility, physical distribution flexibility, and demand management flexibility, as suggested by Devaraj, et al., (2012); Mason & Nair (2012); Fayezi, et al., (2013).

Aziz, et al., (2017) portrays that LF has a significant effect on a company's performance in terms of adding responsive and quality services. In addition to that, Yu, Luo, Feng & Liu (2018) depict that flexibility in distribution and raw material procurement had a positive direct effect on the operational performance of food manufacturers in China. LF also allows businesses to deliver on time with complete transportation documentation, as a result, business and supply chain performance can be improved (Aunyawong et al., 2020). LF in relation to distribution, moreover, affects supply chain flexibility (Singh et al., 2020; Rojo et al., 2016; Luo & Yu, 2016). It is, thus, hypothesized that:

H1: Logistics flexibility positively affects business performance.

H2: Logistics flexibility positively affects supply chain flexibility.

Supply Chain Flexibility

Supply Chain Flexibility (SCF) refers to the quickness of the supply chain in response to changing market conditions (Alamro et al., 2018). In addition, SCF is the ability to adapt to changing circumstances affecting supply chain operations by which leaders, teams and employees in the organization have a joint action plan and operate business for setting strategy and guidelines to minimize potential impacts (Beraha et al., 2018). SCF consists of four components: Product Flexibility, Volume Flexibility, New Product Flexibility, and Responsiveness Flexibility as recommended by Huo, et al., (2018); Singh, et al., (2020); Anning-Dorson (2021).

Additionally, Ko, et al., (2018) found that supplier and logistics flexibilities had a significant positive effect on the manufacturing performance of SMEs. Irfan, et al., (2019) found that quantitative supply flexibility is the mediating variable on the effect of process integration on supply chain agility. In addition, Maqueira, et al., (2020) found that companies use lean manufacturing to achieve supply chain flexibility, leading to an increase in the efficiency of mass production of niche products and better business performance. It is, therefore, hypothesized that:

H3: Supply chain flexibility positively mediates the effect of logistics flexibility on business performance.

Environmental Uncertainty

Environmental Uncertainty (EU) refers to factors affecting business operations caused by environmental uncertainty, which can be considered into 2 levels: first, the environmental uncertainty at the macro level, such as technological changes, economic conditions, political stability, government policies, social change including the natural environment, and second, the environmental uncertainty at the micro level, such as competitive conditions in the business, tastes and preferences of customers, raw material suppliers, competitors, and alternative goods or services (Boon‐itt & Wong, 2011; Wang & Lee, 2013). EU consists of three dimensions: Demand Uncertainty, Supply Uncertainty, and Technological Uncertainty, as conceptualized by Wu (2013); Hong, et al., (2018); Chai, et al., (2019); Jilke (2020).

The results of most past studies consistently describe that environmental uncertainty is positively affect logistics and supply chain flexibilities (Yu et al., 2018; Mishra, 2020, Shukor et al., 2020). Environmental uncertainty also drives businesses to produce new products or upgrade existing products to be better to meet market changes according to customer demand by using sensible costs and handling the time commendably (Luo & Yu, 2016; Mishra & Mishra, 2019; Üstündag & Ungan, 2020). These may lead to business performance improvements. It is, accordingly, hypothesized that:

H4: Environmental Uncertainty positively moderates the effect of logistics flexibility on business performance.

H5: Environmental Uncertainty positively moderates the effect of supply chain flexibility on business performance.

Figure 1: Conceptual Framework

Research Methodology

Population was 72,629 companies registered in the EEC (Department of Business Development, 2020). The sample consisted of 360 companies in the EEC, calculating from the sample size not less than 20 times the observed variable in the model (14x20) as suggested by Hair et al., (2010). Stratified sampling by provincial area using proportional allocation was used.

A questionnaire and interview form was an instrument used as research instrument to collect data. The instrument accuracy was checked for validity and reliability. The validity consisted of content validity using IOC >0.5 and construct validity using confirmatory factor analysis (CFA), while the reliability was checked for Cronbachs’ alpha coefficient >0.8 (Cronbach, 1990). The five-point Likert scale questionnaire had 57 items, comprised the 24 logistics flexibility items, as developed from Aunyawong, et al., (2020), the 12 supply chain flexibility items, as developed from Mishra (2018), the 12 environmental uncertainty items, as developed from Wong & Boon‐itt (2011) and Jilke (2020), and 9 Business Performance items, as developed from Ilmudeen, et al., (2019). For quantitative data interpretation, the criteria recommended by Best & Kahn (2006) was used.

For data analysis, first, the level of variables in the research were analyzed using descriptive statistics, including mean, standard deviation (S.D.), Skewness (Sk), and Kurtosis (Ku) to measure the normal distribution of data in the analysis of SEM. Second, discriminant validity was tested based on Maximum Shared Variance (MSV) < Average Variance Extracted (AVE) and Average Squared Shared Variance (ASV) < AVE; Also, the latent variable's AVE should be greater than the squared correlation between the latent variable and all other variables. In addition, the convergent validity was tested taking into account that the composite reliability value must be greater than 0.7 and AVE > 0.5 (Fornell & Larcker, 1981). Third, CFA was used to check the construct validity of the questionnaire. Forth, path analysis was used to examine the research hypotheses. Fifth, model fit indicators and the criteria of C.R. or t-value >1.96 were based on hypothesis s proposition by Diamantopoulos & Siguaw (2000).

Research Results

Testing Results of Measurement Model

The results showed that the mean of observed variables was from 4.02 to 4.45, which were at a high level, and had a standard deviation (S.D.) from .55 to .64 by considering the skewness (Sk) with values between -3 and +3 and kurtosis (Ku) of less than 8, indicating a normal distribution (Kline, 2005). The factor loadings of all variables were positive and significantly different from zero at the .001 level. The variable with the highest factor loading (.921) was Physical Distribution Flexibility (PDF), .921, the variable with the lowest factor loading (.687) was New Product Flexibility (NPF), and R-square was from .471 to .848, as shown in Table 1.

Table 1
Testing Results of Measurement Model
Variable S.D. Remark Sk Ku b β S.E. t R2
LF 4.19 0.14 High - - - - - - -
PSF 4.23 0.59 High -0.702 0.289 0.940 0.864 0.043 21.910*** 0.747
PUF 4.02 0.63 High -0.487 0.085 0.997 0.860 0.042 23.536*** 0.740
PDF 4.15 0.59 High -0.740 0.857 1.000 0.921 - - 0.848
DMF 4.35 0.48 High -0.312 0.628 0.824 0.839 0.048 22.569*** 0.719
SCF 4.33 0.09 High - - - - - - -
PF 4.31 0.63 High -0.903 1.637 0.930 0.814 0.049 19.019*** 0.662
VF 4.24 0.63 High -0.875 1.443 1.000 0.863 - - 0.745
NPF 4.45 0.54 High -0.879 1.475 0.674 0.686 0.046 14.653*** 0.471
RF 4.33 0.59 High -0.652 1.420 0.780 0.730 0.060 16.308*** 0.533
EU 4.23 0.01 High - - - - - - -
DU 4.23 0.64 High -0.767 0.683 0.979 0.743 0.060 16.270*** 0.553
SU 4.23 0.57 High -0.551 0.194 0.806 0.695 .0048 16.659*** 0.483
TU 4.24 0.59 High -0.848 0.847 1.000 0.831 - - 0.690
BP 4.19 0.08 High - - - - - - -
FA 4.28 0.52 High -0.411 1.297 0.753 0.872 0.037 20.205*** 0.760
OE 4.16 0.67 High -0.454 1.905 0.931 0.834 0.040 23.074*** 0.696
MP 4.14 0.70 High -0.580 1.407 1.000 0.849 - - 0.720

Direct and Mediation Effect Analysis

As shown in Table 2 and Figure 2, the results of model’s structural validity by analyzing the structural equations found that the model was consistent with the empirical data since the model fit indicators depicted Chi-Square (CMIN) = 40.072, df = 29, p-value =0.083 (>0.05 level), c2/df = 1.382, (< 2), as well as GFI =0.972, AGFI =0.942, TLI =0.989, CFI =0.994 (>0.90), and RMSEA =0.007, RMR = .041 (<0.05). In addition, the results portrayed that all the variable's factor loadings had a positive value and was statistically different from zero at the 0.001 level, indicating an acceptance of H1-H3 with statistical significance at the .001 level.

Figure 2: Structural Equation Model

Table 2
Path Analysis Results
H IndependentVariable Mediating Variable DependentVariable β p-value S.E. t-value(C.R.) R2
H1 LF - BP 0.207 0.000 0.023 4.979 0.720
H2 LF - SCF 0.896 0.000 0.21 5.257 0.728
H3 LF SCF BP 0.186 0.000 - - -

Moderation Effect Analysis

As shown in Table 3, the results of model’s structural validity by analyzing moderating effect reveal that the moderating effect of environmental instability (EU) on the effect of logistics flexibility (LF) and supply chain flexibility (SCF) on business performance (BP) had positive effect size of .029 and .018, respectively, with statistical significance at the .001 level, plus R-square of .720 and .728, indicating an acceptance of H4 and H5 with statistical significance at the .001 level.

Table 3
Moderating Effects of Environmental Uncertainty on Business Performance
Structural Path Environmental Uncertainty
H IndependentVariable DependentVariable β p-value S.E. t-value(C.R.) R2
H4 LF BP .029 .000 .023 4.979 .720
H5 SCF BP .018 .000 .021 5.257 .728

Discussion and Conclusion

According to the present study findings, supply chain flexibility acts as the most significant contributors to business performance, while the effect of logistics flexibility is lower, however, statistically significant. The supply chain flexibility, moreover, has a mediating role, whereas environmental uncertainty has a moderating role in the theoretical model developed. This reflects that the logistics and supply chain flexibilities firms adopt may lead to business performance improvement and higher business performance under high environmental uncertainty. Based on hypothesis testing, the results show that first, logistics flexibility has a direct positive effect on business performance, consistent with Aziz, et al., (2018); Yu, et al., (2018), which identifies that logistics flexibility has a huge impact on a company's performance in terms of adding responsive and flexible services, and Yu, et al., (2018) which depicts that flexibility in distribution and raw material procurement has a significant effect on the operational performance of Chinese food manufacturers. It was also consistent with Aunyawong, et al., (2020), which represents that logistics flexibility allows businesses to deliver products on time. Second, the results display that logistics flexibility has a direct positive effect on supply chain flexibility, consistent with Singh, et al., (2020); Rojo, et al., (2016); Luo & Yu, (2016) which denote that logistics distribution flexibility distresses logistics flexibility. Third, supply chain flexibility mediates the effect of logistics flexibility on business performance, in line with Ko, et al., (2018) which signify that supply chain flexibility, consisting of supplier resilience and logistics flexibility, has an identical positive impact on SMEs' productivity performance, while (Irfan et al., 2019) connote that quantitative supply flexibility has a mediating role on the effect of process integration on supply chain agility. In addition, Maqueira, et al., (2020) found that companies use lean manufacturing to achieve supply chain flexibility, leading to an increase in the competence of mass production of niche products and improved business performance.

Furthermore, logistics flexibility, forth, the environmental uncertainty moderates the effect of logistics flexibility on business performance, consistent with Yu, et al., (2018), Mishra, (2020); Shukor, et al., (2020), which discover that uncertainties of technology, competition and demand and supply force business to increase flexibility in procurement, delivery and distribution. Fifth, environmental uncertainty moderates the effect of supply chain flexibility on business performance, in line with Luo & Yu, (2016), Mishra & Mishra, (2019); Üstündag & Ungan (2020) which notice that environmental uncertainty also drives businesses to produce new products or upgrade existing products to be better to meet market changes according to customer demand by using reasonable costs and managing time effectively.

The study recommends that relevant government agencies should jointly provide training for EEC entrepreneurs on flexible logistics and supply chain management under an uncertain business environment because when entrepreneurs have more such knowledge, it inevitably leads to a better business performance. The government, besides, should co-invest with entrepreneurs who need capital assistance, cooperation among various private sectors to help businesses in the EEC and promote the stable country's economy. The further research should be studied on other factors, such as supply chain integration, green supply chain management practices, environmental performance, etc., since these factors could affect business performance.

Acknowledgement

Wissawa Aunyawong is the corresponding author. His email address is wissawa.au@ssru.ac.th.

References

  1. Alamro, A.S., Awwad, A.S., & Anouze, A.L.M. (2018). The integrated impact of new product and market flexibilities on operational performance: The case of the Jordanian manufacturing sector. Journal of Manufacturing Technology Management, 29(7), 1163-1187.
  2. Anning-Dorson, T. (2021). Organizational culture and leadership as antecedents to organizational flexibility: implications for SME competitiveness. Journal of Entrepreneurship in Emerging Economies,
  3. https://doi.org/10.1108/JEEE-08-2020-0288.
  4. Aunyawong, W., Wararatchai, P., & Hotrawaisaya, C. (2020). The influence of supply chain integration on supply chain performance of auto-parts manufacturers in Thailand: A Mediation Approach. International Journal of Supply Chain Management, 9(3), 578-590.
  5. Aziz, Z.A., Razak, R.C., Hussin, N.S.N., & Yacobb, M.R. (2017). The relationship of logistics flexibility and value-added capability on logistics performance in logistics services. In Proceeding of the 2017 UMK Postgraduate Colloquium: Cultivating Excellence through Research (Unpaged). Kelantan: University of Malaysia Kelantan.
  6. Beraha, A., Bingol, D., Ozkan-Canbolat, E., & Szczygiel, N. (2018). The effect of strategic flexibility configurations on product innovation. European Journal of Management and Business Economics, 27(2), 129-140.
  7. Boon‐itt, S., & Yew Wong, C. (2011). The moderating effects of technological and demand uncertainties on the relationship between supply chain integration and customer delivery performance. International Journal of Physical Distribution & Logistics Management, 41(3), 253-276.
  8. Chai, L., Li, J., Clauss, T., & Tangpong, C. (2019). The influences of interdependence, opportunism and technology uncertainty on interfirm coopetition. Journal of Business & Industrial Marketing, 34(5), 948-964.
  9. Chienwattanasook, K., Wattanapongphasuk, W., Prianto, A., & Jermsittiparsert, K. (2019).Corporate Entrepreneurship and Business Performance of Logistic Companies in Indonesia. Industrial Engineering & Management Systems, 18(3), 541-550.
  10. Dabic, M., Maley, J., & Novak, I. (2020). An analysis of globalisation in international business research 1993–2018: rise of the sceptics. Critical perspectives on international business, https://doi.org/10.1108/cpoib-02-2019-0016.
  11. Department of Business Development. (2020). Business information in the Eastern Economic Corridor Development Area. Bangkok: Ministry of Commerce.
  12. Diamantopoulos, A., & Siguaw, J.A. (2000). Introducing LISREL. London: Sage Publications.
  13. Goldberg, P. (2019). How important are Global Value Chains for development? Read the new WDR2020 draft report and comment. Retrieved from https://blogs.worldbank.org/developmenttalk/how-important-are-global-value-chains-development-read-new-wdr2020-draft-report-and
  14. Gordon, L.A., & Narayanan, V.K. (1984). Management accounting systems, perceived environmental uncertainty and organization structure: An empirical investigation. Accounting, Organizations and Society, 9(1), 33-47.
  15. Govindarajan, V. (1984). Appropriateness of accounting data in performance evaluation: An empirical examination of environmental uncertainty as an intervening variable. Accounting, Organizations and Society, 9(2), 125-135.
  16. Hair, J.F., Black, W.C., Babin, B.J., & Anderson, R.E. (2010). Multivariate Data Analysis.(7th ed.). Upper Saddle River, NJ: Prentice Hall.
  17. Hong, Z., Lee, C.K.M., & Zhang, L. (2018). Procurement risk management under uncertainty: a review. Industrial Management & Data Systems, 118(7), 1547-1574.
  18. Huo, B., & Wang, Z. (2018). Supply chain flexibility concepts, dimensions and outcomes: an organisational capability perspective. International Journal of Production Research, 56(17), 5883-5903.
  19. Ilmudeen, A., Bao, Y., & Alharbi, I.M. (2019). How does business-IT strategic alignment dimension impact on organizational performance measures: Conjecture and empirical analysis. Journal of Enterprise Information Management, 32(3), 457-476.
  20. Irfan, M., Wang, M., & Akhtar, N. (2019). Enabling supply chain agility through process integration and supply flexibility: Evidence from the fashion industry. Asia Pacific Journal of Marketing and Logistics, 32(2), 519-547.
  21. Jermsittiparsert, K. (2021). Role of sustainable supply chain, marketing performance and information availability in sustainable business performance. Journal of Management Information and Decision Sciences, 24(S1), 36.
  22. Jilke, S. (2020). Impact of technological uncertainty and technological complexity on organizational information processing capability: the moderating role of work experience. European Journal of Innovation Management.
  23. Kafetzopoulos, D., Psomas, E., & Skalkos, D. (2020). Innovation dimensions and business performance under environmental uncertainty. European Journal of Innovation Management, 23(5), 856-876.
  24. Ko, W.W.J., Liu, G., Ngugi, I.K., & Chapleo, C. (2018). External supply chain flexibility and product innovation performance: A study of small- and medium-sized UK-based manufacturers. European Journal of Marketing, 52(9/10), 1981-2004.
  25. Lawrence, P.R., & Lorsch, J.W. (1967). Organization and environment. Boston: Harvard graduate school of business administration.
  26. Luo, B.N., &Yu, K.(2016). Fits and misfits of supply chain flexibility to environmental uncertainty: Two types of asymmetric effects on performance. The International Journal of Logistics Management, 27(3), 862-885.
  27. Maqueira, J.M., Novais, L.R., & Bruque, S. (2020). Total eclipse on business performance and mass personalization: how supply chain flexibility eclipses lean production direct effect. Supply Chain Management, 26(2), 256-278.
  28. Mee-ngoen, B., Sirariyakul, T., Limphothong, S., Tomcharoen, N., & Jermsittiparsert, K. (2020). Innovativeness as antecedents to firm performance: The mediating role of competitive advantage and supply chain flexibility of manufacturing firms. International Journal of Supply Chain Management, 9(2), 385-392.
  29. Mishra, R. (2018). Configuration of volume flexibility in Indian manufacturing firms: evidence from case studies. International Journal of Quality & Reliability Management, 35(1), 232-265.
  30. Mishra, R., & Mishra, O.N. (2019). Prioritising dimensions of entrepreneurial orientation for supply chain flexibility development in an uncertain environment. Journal of Manufacturing Technology Management, 30(2), 483-505.
  31. Mishra, R. (2020). A hybrid multi-criteria decision-making approach to assess the enablers of manufacturing flexibility under fuzzy environment. International Journal of Quality & Reliability Management, https://doi.org/10.1108/IJQRM-03-2020-0075
  32. National Statistical Office. (2020). Income per capita. Retrieved from https://opendata.data.go.th/dataset/nso2598.
  33. Phrapratanporn, B., Wararatchai, P., Aunyawong, W., & Rashid, N.R.N.A. (2019). Enhancing supply chain performance of SMEs in Thailand using the integrated personnel development model. International Journal of Supply Chain Management, 8(5), 176-186.
  34. Shukor, A.A.A., Newaz, M.S., Rahman, M.K., & Taha, A.Z. (2020). Supply chain integration and its impact on supply chain agility and organizational flexibility in manufacturing firms. International Journal of Emerging Markets.
  35. Singh, R.K., Acharya, P., & Modgil, S. (2020). A template-based approach to measure supply chain flexibility: a case study of Indian soap manufacturing firm. Measuring Business Excellence, 24(2), 161-181.
  36. Song, X. M., & Montoya-Weiss, M. M. (2001). The effect of perceived technological uncertainty on Japanese new product development.Academy of Management Journal,44(1), 61-80.
  37. Soonthornpipit, H., Kortana, T., & Aunyawong, W. (2021). expansion opportunities of community enterprises in Thailand to China. Psychology and Education Journal, 57(8), 86-97.
  38. The Eastern Economic Corridor Policy Office. (2020). Overview of EEC Development. Retrieved from https://www.eeco.or.th/th/eec-development-plan
  39. Tiwari, A. K., Tiwari, A., Samuel, C., & Bhardwaj, P. (2015). A literature review on empirical studies in supply chain flexibility. IJAMS, 15(2), 93-104.
  40. Üstündag, A., & Ungan, M.C. (2020). Supplier flexibility and performance: an empirical research. Business Process Management Journal, 26(7), 1851-1870.
  41. Wang, E.T.G., & Chueh‐An Lee, N. (2013). Using power regimes to explore perceived environmental uncertainty. Industrial Management & Data Systems, 113(7), 950-966.
  42. Wu, G. (2013). The influence of green supply chain integration and environmental uncertainty on green innovation in Taiwan's IT industry. Supply Chain Management, 18(5), 539-552.
  43. Yu, K., Cadeaux, J., Luo, N., Qian, C., & Chen, Z. (2018). The role of the consistency between objective and perceived environmental uncertainty in supply chain risk management. Industrial Management & Data Systems, 118(7), 1365-1387.
  44. Yu, K., Luo, B.N., Feng, X., & Liu, J. (2018). Supply chain information integration, flexibility, and operational performance: An archival search and content analysis. The International Journal of Logistics Management, 29 (1), 340-364.
Get the App