Aug 2026· European Journal of Economic and Financial Research· Vol 10· 0 citations· 99 references
Abstract
This study evaluated the impact of descriptive data analytics on the supply chain performance of agritech companies in Kenya. The study was anchored on the Resource Dependence Theory and adopted a descriptive research design. The target population comprised 315 supply chain and information technology officers, from whom a sample of 172 respondents was selected using Yamane's (1967) formula. Primary data were collected using structured questionnaires and analyzed using correlation and linear regression techniques. The findings revealed that descriptive data analytics had a positive and statistically significant effect on fresh food supply chain performance. The study concludes that descriptive data analytics enhances supply chain performance by improving visibility, monitoring, and evidence-based decision-making within agritech firms. The study recommends that agritech companies invest in integrated data management systems, strengthen data quality practices, and build employees' analytical capabilities to maximize the benefits of descriptive analytics and improve overall supply chain performance. JEL: M11, L23, L14, R41
This study assessed the effect of predictive data analytics on the supply chain performance of Agritech Companies in Kenya. The study was grounded in Organizational Information Processing Theory. A descriptive research design was adopted. The target population consisted of 315 supply chain and information technology officers, from which a sample of 172 respondents was selected using Yamane’s formula (1967). Data were collected using structured questionnaires and interviews. Analysis was conducted using correlation analysis, and multiple regression analysis. The findings revealed that predictive data analytics had a positive and significant effect on food supply chain performance. The study findings indicated that predictive analytics improved demand forecasting and planning accuracy. The study concluded that predictive data analytics significantly enhances food supply chain performance, particularly when supported by strong organizational capabilities. It recommended that agritech firms invest in predictive data analytics systems to improve effective utilization of analytics tools in supply chain operations. JEL: M11, L23, L14, R41
Omuyoyi Maureen Matanga, Denis Ouma, Nurwin Fozia· European Journal of Economic...· 0 citations
This study investigates how big data analytics capability appears to support competitive strategy development in the Palestinian dairy industry, focusing on the role of data utilization in developing organizational capabilities, creating value and positioning the firm strategically.
This study adopts an exploratory multiple-case study approach supported by descriptive survey evidence and Visualization-Based Pattern Analysis. Data were collected through unstructured interviews with managers and employees from leading Palestinian dairy companies, alongside descriptive questionnaire-based evidence. The qualitative findings were analyzed using thematic and VRIO-based analysis to identify patterns related to big data practices, capability development, organizational maturity and competitive strategy dimensions.
The findings indicate that participating firms recognize the value of data for customer understanding, product development and operational decision-making. However, analytics practices remain fragmented and rely largely on conventional or manual processes. Limited system integration, analytical expertise, data governance and real-time processing restrict the incorporation of analytics into organizational routines and strategic decision-making. These conditions reflect a gap between awareness of data's potential value and the development of integrated analytics capabilities.
Managers should prioritize the gradual integration of customer, operational and market data, supported by appropriate infrastructure, analytical skills and governance procedures. These measures may improve the consistency of data-informed decision-making, operational coordination, customer responsiveness and market positioning.
This study provides context-specific empirical evidence on data utilization and analytics capability development within the Palestinian dairy industry, an underexplored resource-constrained setting. Its primary contribution is contextual rather than the development of a new theoretical construct. The study uses the term data maturity gap as an interpretive lens to organize evidence on the disconnect between firms' recognition of data value and their ability to integrate analytical, technological and organizational capabilities. The findings illustrate how mechanisms already identified by the Resource-Based View, Dynamic Capabilities Theory, absorptive capacity and analytics maturity research operate within this particular industrial context.
Unknown authors· Journal of Enterprise Inform...· 0 citations
The growing demand for sustainability in manufacturing and the inefficiencies of traditional methods in the supply chain highlight the importance of finding smarter solutions. Even with the growing availability of digital tools and AI technologies, organizations are yet to fully utilize them to aid sustainability efforts, causing inefficient use of resources, waste, and environmental damage. The chapter examined AI supply chains optimizing for sustainability: evidence from Nigeria manufacturing industry. The chapter utilized a survey research design with a population of 950 supply chain employees in Nigerian Breweries, Lagos State, with an estimated sample size of 281 using the Yamane (1967) formula. Data was collected using a structured questionnaire with a five-point Likert scale. Descriptive statistics and multiple regression analysis were used to analyze the data in SPSS version 27. The findings showed that AI-driven demand forecasting (coefficient = 4.287) and AI-based inventory optimization (coefficient = 3.549) have a positive significant on sustainability, with 56.7% of the variance in sustainability outcomes. The chapter concluded that AI-driven supply chain optimization plays a significant role in minimizing waste, optimizing resources, and meeting market demand. The study recommended that Nigerian Breweries should utilize AI-powered demand forecasting tools for production planning and should implement AI-based inventory optimization systems to ensure that inventory levels are optimized and reducing excess stock.
Dolapo Stephen Akinwumi, A. Salau· Al-Zaytoonah University Jour...· 0 citations
The fruit supply chain plays a vital role in ensuring food security, generating income, and improving livelihoods in rapidly growing urban centers. However, the distribution of perishable fruits continues to face significant challenges, including limited access to market information, inadequate credit facilities, weak marketing practices, and inefficient intermediary activities. These constraints contribute to post-harvest losses, supply disruptions, and reduced profitability across the supply chain. This study examined the determinants of the performance of the perishable fruit supply chain at Mabibo Market. Specifically, the study examined the effects of market information availability, credit accessibility, vendor marketing practices, and intermediary activities on supply chain performance. A cross-sectional research design was employed, integrating quantitative and qualitative approaches. Quantitative data were collected from 120 respondents. The quantitative data were analyzed using descriptive statistics and Partial Least Squares Structural Equation Modeling (PLS-SEM), while qualitative data were subjected to thematic content analysis. The findings revealed that market information significantly reduced post-harvest losses (β = –0.39, p < 0.01), highlighting the importance of timely and accurate information in improving decision-making and market responsiveness. Access to credit positively influenced supply reliability (β = 0.41, p < 0.01) by enabling investment in storage, transportation, and related operational activities. Vendor marketing practices significantly improved sales performance and reduced spoilage (β = 0.35, p < 0.05). Conversely, intermediary activities negatively affected supply chain performance by increasing transaction costs and reducing producer margins (β = –0.27, p < 0.05). The study concludes that strengthening market information systems, expanding access to affordable credit, improving vendor marketing capabilities, and enhancing transparency in intermediary operations are critical for improving supply chain performance. It recommends the adoption of digital market information platforms, affordable financing schemes, vendor training programs, and regulatory frameworks for intermediary operations to reduce post-harvest losses and enhance urban food security in Tanzania.
William Mwalimu· International Journal For Mu...· 0 citations
Purpose: This study investigates the impact of four green supply chain management (GSCM) practices—eco-design, green procurement, green manufacturing, and green distribution—on firm performance, while examining the mediating role of sustainable supply chain management (SSCM) among manufacturing firms in Pakistan's textile industry.
Approach: Grounded in a positivist research philosophy, the study adopts a quantitative, cross-sectional survey design. Data were collected through a structured, five-point Likert-scale questionnaire administered to 150 managers, supervisors, and supply chain professionals working in textile manufacturing firms located in Faisalabad, Sheikhupura, and Multan, using a convenience sampling technique. Data were analysed using SPSS for descriptive statistics, reliability analysis, and Pearson correlation, and AMOS for confirmatory factor analysis, structural equation modelling (SEM), and bootstrapped mediation testing.
Findings: The measurement model demonstrated sound reliability (overall Cronbach's α = .913) and adequate sampling adequacy (KMO = .843, Bartlett's test significant, p < .001). Eco-design (β = .410), green procurement (β = .194), green manufacturing (β = .191), and green distribution (β = .165) each exerted a significant positive effect on firm performance, and SSCM itself was a strong positive predictor of firm performance (β = .678). However, SSCM significantly mediated only the relationship between eco-design and firm performance (β = .329, p < .001); its mediating role in the relationships involving green procurement, green manufacturing, and green distribution was not statistically supported.
Contributions: The study extends the Resource-Based View by demonstrating that GSCM practices function as valuable, rare, and difficult-to-imitate resources that translate into performance gains, partly through sustainable supply chain capabilities. Textile manufacturers should prioritize eco-design initiatives and strengthen the sustainable supply chain infrastructure required to convert broader green practices into measurable performance outcomes.
Originality value: This is among the first studies to empirically test SSCM as a mediating mechanism between multiple GSCM dimensions and firm performance within Pakistan's textile sector, an emerging-economy context where such evidence remains scarce.
References
Afum, E., Osei-Ahenkan, V. Y., Agyabeng-Mensah, Y., Owusu, J. A., Kusi, L. Y., & Ankomah, J. (2020). Green manufacturing practices and sustainable performance among Ghanaian manufacturing SMEs: The explanatory link of green supply chain integration. Management of Environmental Quality: An International Journal, 31(6), 1457–1475. https://doi.org/10.1108/MEQ-01-2020-0011
Akbari, M., & McClelland, R. (2020). Corporate social responsibility and corporate citizenship in sustainable supply chain: A structured literature review. Benchmarking: An International Journal, 27(6), 1274–1301. https://doi.org/10.1108/BIJ-11-2019-0522
Al Humdan, E., Shi, Y., & Behnia, M. (2020). Supply chain agility: A systematic review of definitions, enablers, and performance implications. International Journal of Physical Distribution & Logistics Management, 50(2), 342–368. https://doi.org/10.1108/IJPDLM-06-2019-0194
Azevedo, S. G., Carvalho, H., & Machado, V. C. (2011). The influence of green practices on supply chain performance: A case study approach. Transportation Research Part E: Logistics and Transportation Review, 47(6), 850–871. https://doi.org/10.1016/j.tre.2011.05.017
Bernstein, I. H., & Nunnally, J. C. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Bollen, K. A. (1989). Structural equations with latent variables. John Wiley & Sons.
Carter, C. R., & Carter, J. R. (1998). Interorganizizational determinants of environmental purchasing: Initial evidence from the consumer products industries. Decision Sciences, 29(3), 659–684. https://doi.org/10.1111/j.1540-5915.1998.tb01358.x
Carvalho, H., Azevedo, S. G., & Machado, V. C. (2012). Agile and resilient approaches to supply chain management: Influence on performance and competitiveness. Logistics Research, 4(1–2), 49–62. https://doi.org/10.1007/s12159-012-0069-8
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
Field, A. (2009). Discovering statistics using SPSS (3rd ed.). Sage.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson.
Hamdy, O. M. M., Elsayed, K. K., & Elahmady, B. (2018). Impact of sustainable supply chain management practices on Egyptian companies' performance. Journal of Business and Retail Management Research, 12(3), 88–98.
Hart, S. L. (1995). A natural-resource-based view of the firm. Academy of Management Review, 20(4), 986–1014. https://doi.org/10.2307/258963
Hoyle, R. H. (Ed.). (1995). Structural equation modeling: Concepts, issues, and applications. Sage.
Kong, T., Feng, T., Huang, Y., & Cai, J. (2020). How to convert green supply chain integration efforts into green innovation: A perspective of the knowledge-based view. Sustainable Development, 28(5), 1106–1121. https://doi.org/10.1002/sd.2071
Min, H., & Galle, W. P. (2001). Green purchasing practices of U.S. firms. International Journal of Operations & Production Management, 21(9), 1222–1238. https://doi.org/10.1108/EUM0000000005923
Ninlawan, C., Seksan, P., Tossapol, K., & Pilada, W. (2010, March 17–19). The implementation of green supply chain management practices in electronics industry. In Proceedings of the International MultiConference of Engineers and Computer Scientists 2010 (IMECS 2010) (Vol. 3, pp. 1563–1568). Hong Kong.
Rao, P., & Holt, D. (2005). Do green supply chains lead to competitiveness and economic performance? International Journal of Operations & Production Management, 25(9), 898–916. https://doi.org/10.1108/01443570510613956
Zhu, Q., & Sarkis, J. (2004). Relationships between operational practices and performance among early adopters of green supply chain management practices in Chinese manufacturing enterprises. Journal of Operations Management, 22(3), 265–289. https://doi.org/10.1016/j.jom.2004.01.005
Zimon, D., Madzik, P., & Sroufe, R. (2020). The influence of ISO 9001 and ISO 14001 on sustainable supply chain management in the textile industry. Sustainability, 12(10), Article 4282. https://doi.org/10.3390/su12104282
Saba Tanveer· Journal of Business Insight...· 0 citations
This study aims to examine how the implementation of big data analytics (BDA) influences operational performance and supply chain resilience in China's manufacturing sector. It aims to explain the mechanisms through which BDA contributes to both immediate efficiency gains and long-term adaptability, drawing on the Dynamic Capabilities Theory (DCT).
A mixed-method approach was adopted. Quantitative analysis was conducted using survey data from 304 manufacturing firms, analyzed through structural equation modelling. To complement the statistical findings, two in-depth case studies were carried out to provide contextual insights into how BDA initiatives are implemented and managed in practice.
The results show that BDA implementation significantly enhances operational performance and supply chain resilience. Dynamic capabilities partially mediate the relationship between BDA adoption and supply chain resilience, indicating that BDA not only drives short-term operational improvements but also fosters long-term strategic adaptability. Case study evidence further demonstrates that firms' motivations, process focus, and implementation strategies shape the performance outcomes of BDA adoption.
The study focuses on manufacturing firms in China, which may limit the generalisability of the findings to other sectors or regions. Future research could explore cross-country comparisons and longitudinal designs to capture the evolving role of BDA and dynamic capabilities in global supply chain resilience.
This study advances understanding of BDA by showing that its strategic value extends beyond process efficiency to the development of dynamic capabilities that enhance long-term agility and adaptability. It demonstrates that performance outcomes depend on how BDA is strategically aligned with organisational priorities, while strategic integration drives sustained competitive advantage. The research reframes BDA as a capability enabler rather than a technological tool, emphasizing the need for learning environments, predictive analysis, collaborative practices, and data-driven decision-making to unlock its transformative potential for supply chain resilience and competitiveness.
Ying Xie, Yahui Chen, Teng Teng et al.· International Journal of Log...· 0 citations
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