THE EFFECT OF PREDICTIVE DATA ANALYTICS ON FRESH FOOD SUPPLY CHAIN PERFORMANCE FOR AGRITECH COMPANIES IN KENYA
Abstract
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
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AFDB. (2017). AfDB boosts African Agriculture and nutrition with US$ 10 million equity investment in Phatisa Food Fund. Retrieved from https://www.afdb.org/ar/news-and-events/afdb-boosts-african-agriculture-and-nutrition-with-us-10-million-equity-investment-in-phatisa-food-fund-17587
Aguinis, H., Beaty, J. C., Boik, R. J., & Pierce, C. A. (2005). Effect size and power in assessing moderating effects of categorical variables using multiple regression: a 30-year review. Journal of applied psychology, 90(1), 94. https://doi.org/10.1037/0021-9010.90.1.94
Ajayi, V. O. (2017). Primary Sources of Data and Secondary Sources of Data. Benue State University, 1, 1-6. Retrieved from https://www.researchgate.net/publication/320010397_Primary_Sources_of_Data_and_Secondary_Sources_of_Data
Ajibade, P. (2018). Technology acceptance model limitations and criticisms: Exploring the practical applications and use in technology-related studies, mixed-method, and qualitative researches. Library Philosophy and Practice, 9. Retrieved from https://www.researchgate.net/publication/329308172_Technology_Acceptance_Model_Limitations_and_Criticisms_Exploring_the_Practical_Applications_and_Use_in_Technology-related_Studies_Mixed-method_and_Qualitative_Researches
Alao, M., Ogunnaike, O. O., & Adeyemo, O. (2019). The impact of predictive analytics on supply chain management in Nigeria. International Journal of Advanced Computer Science and Applications, 10(6), 61-67. https://doi.org/10.14569/IJACSA.2019.0100609
Amponsah, S. K., & Buertey, S. (2021). Diagnostic analytics and supply chain performance in Ghana: The moderating role of supply chain agility. Journal of Business Research, 132, 390-401.
Anand, N., & Grover, N. (2015). Measuring retail supply chain performance: Theoretical model using key performance indicators (KPIs). Benchmarking: An international journal 22(1). https://doi.org/10.1108/BIJ-05-2012-0034
Asadoorian, M. O., & Kantarelis, D. (2005). Essentials of inferential statistics. University Press of America.
Asiamah, N., Mensah, H., & Oteng-Abayie, E. F. (2017). General, target, and accessible population: Demystifying the concepts for effective sampling. The qualitative report, 22(6), 1607-1621. Retrieved from https://doi.org/10.46743/2160-3715/2017.2674
Bag, S., Wood, L. C., Xu, L., Dhamija, P., & Kayikci, Y. (2020). Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. Resources, Conservation and Recycling, 153. https://doi.org/10.1016/j.resconrec.2019.104559
Bahrami, M., Shokouhyar, S., & Seifian, A. (2022). Big data analytics capability and supply chain performance: the mediating roles of supply chain resilience and innovation. Modern Supply Chain Research and Applications 4(1). https://doi.org/10.1108/MSCRA-11-2021-0021
Banerjee, A., & Chaudhury, S. (2010). Statistics without tears: Populations and samples. Industrial Psychiatry Journal, 19(1), 60. https://doi.org/10.4103/0972-6748.77642
Banerjee, M., & Mishra, M. (2017). Retail supply chain management practices in India: A business intelligence perspective. Journal of Retailing and Consumer Services, 34, 248-259. https://doi.org/10.1016/j.jretconser.2015.09.009
Baqleh, L. A., & Alateeq, M. M. (2023). The Impact of Supply Chain Management Practices on Competitive Advantage: The Moderating Role of Big Data Analytics. International Journal of Professional Business Review, 8(3), e0679-e0679. https://doi.org/10.26668/businessreview/2023.v8i3.679
Barga, R., Fontama, V., Tok, W. H., & Cabrera-Cordon, L. (2015). Predictive analytics with Microsoft Azure machine learning (pp. 221-241). Berkeley: Apress. https://doi.org/10.1007/978-1-4842-0445-0
Berger, M., & van Helvoirt, B. (2018). Ensuring food secure cities–retail modernization and policy implications in Nairobi, Kenya. Food Policy, 79, 12-22. https://doi.org/10.1016/j.foodpol.2018.04.004
Berger, V. W., & Zhou, Y. (2014). Kolmogorov–Smirnov test: Overview. Wiley statsref: Statistics reference online. https://doi.org/10.1002/9781118445112.stat06558
Boberg, C. (2017). Surveys: The use of written questionnaires in sociolinguistics. In Data Collection in Sociolinguistics (pp. 134-143). Routledge. https://doi.org/10.4324/9781315535258-28
Bollen, K. A., Biemer, P. P., Karr, A. F., Tueller, S., & Berzofsky, M. E. (2016). Are survey weights needed? A review of diagnostic tests in regression analysis. Annual Review of Statistics and Its Application, 3, 375-392. https://doi.org/10.1146/annurev-statistics-011516-012958
Boone, T., Ganeshan, R., Jain, A., & Sanders, N. R. (2019). Forecasting sales in the supply chain: Consumer analytics in the big data era. International Journal of Forecasting, 35(1), 170-180. https://doi.org/10.1016/j.ijforecast.2018.09.003
Bryman, A. (2012). Social research methods (4th ed.). Oxford University Press. Retrieved from https://books.google.ro/books/about/Social_Research_Methods.html?id=vCq5m2hPkOMC&redir_esc=y
Bryman, A., & Cramer, D. (2009). Quantitative data analysis with SPSS 14, 15 & 16: A guide for social scientists. Routledge/Taylor & Francis Group. Retrieved from https://www.researchgate.net/publication/284300766_Quantitative_data_analysis_with_spss_1415_and_16_a_guide_for_social_scientists
Chakravarthy, B., & Coughlan, S. (2011). Emerging market strategy: innovating both products and delivery systems. Strategy & Leadership, 40(1), 27-32. https://doi.org/10.1108/10878571211191675
Chen, J., & Chen, W. (2019). Descriptive analytics in retail supply chain management: A case study of Walmart. Journal of Supply Chain Management Systems, 8(2), 38-49. https://doi.org/10.47604/ijscm.3311
Chen, X., Li, Y., Li, Q., Li, L., & Li, W. (2019). A descriptive analytics framework for supply chain management. Journal of Industrial and Production Engineering, 36(5), 284-294. https://doi.org/10.1080/21681015.2018.1559455
Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches. Sage Publications. Retrieved from https://books.google.com/books/about/Research_Design.html?id=4uB76IC_pOQC
Cytonn Investments. (2019). Kenya Retail Report 2021. Retrieved from https://cytonn.com/topicals/kenya-retail-report
Cytonn Investments. (2020). E-commerce Shaping the Retail Sector. Retrieved from https://research.cytonn.com/research/kenya-retail-report-2020
Cytonn Investments. (2021). Rapid Expansion by Retailers to Cushion the Retail Sector. Retrieved from https://cytonn.com/topicals/kenya-retail-report2#:~:text=The%20Kenyan%20retail%20sector's%20overall,what%20was%20recorded%20in%202021.
Cytonn Investments. (2022). Accelerated Retail Investments. Retrieved from https://cytonnreport.com/topicals/kenya-retail-report-2
Daoud, J. I. (2017, December). Multicollinearity and regression analysis. Journal of Physics: Conference Series Vol. 949, No. 1, p. 012009. https://doi.org/10.1088/1742-6596/949/1/012009
Datareportal.com. (2022). Digital 2022 Kenya. Retrieved from https://datareportal.com/reports/digital-2022-kenya
De Giovanni, P., Mascolo, J., & Miranda, S. (2020). Prescriptive analytics for production planning and control: A case study in the automotive sector. International Journal of Production Economics, 229.
DeGregori, T. R. (2019). Resources are not; they become: An institutional theory. In Evolutionary Economics: v. 1 (pp. 291-313). Routledge. Retrieved from https://www.jstor.org/stable/4225924
Dioha, C., Mohammed, N. A., & Okpanachi, J. (2018). Effect of firm characteristics on profitability of listed consumer goods companies in Nigeria. Journal of Economics and Sustainable Development, 9(8), 34-44. https://doi.org/10.56578/jafas040202
Drees, J. M., & Heugens, P. P. (2013). Synthesizing and extending resource dependence theory: A meta-analysis. Journal of Management, 39(6), 1666-1698. https://doi.org/10.1177/0149206312471391
Dworkin, S. L. (2012). Sample size policy for qualitative studies using in-depth interviews. Archives of sexual behavior, 41, 1319-1320. https://doi.org/10.1007/s10508-012-0016-6
Egbunike, C. F., & Okerekeoti, C. U. (2018). Macroeconomic factors, firm characteristics and financial performance: A study of selected quoted manufacturing firms in Nigeria. Asian Journal of Accounting Research 3(2). https://doi.org/10.1108/AJAR-09-2018-0029
El Hiri, M., En-Nadi, A., & Chafi, A. (2019). Effect of firm characteristics on sources of supply and demand risks: An empirical investigation on Moroccan industrial firms. International Journal of Engineering Research in Africa, 40, 162-170. Retrieved from https://www.scientific.net/JERA.40.162?utm_source=researchgate.net&utm_medium=article
Elsayed, E. A. (2012). Overview of reliability testing. IEEE Transactions on Reliability, 61(2), 282-291. Retrieved from https://doi.org/10.1109/TR.2012.2194190
European Commission. (2021). e-Commerce rules in the EU. Retrieved from https://digital-strategy.ec.europa.eu/en/policies/e-commerce-rules-eu
European Union. (2026). 47 top AgriTech companies and startups in Kenya in January 2026. Retrieved from https://www.f6s.com/companies/agritech/kenya/co
FAO. (2021). Food Retail. Retrieved from https://www.fao.org/fileadmin/user_upload/tci/docs/AH4-Food%20Retail.pdf
Fawcett, S. E., Magnan, G. M., & McCarter, M. W. (2008). Benefits, barriers, and bridges to effective supply chain management. Supply Chain Management: An International Journal, 13(1), 35-48. https://doi.org/10.1108/13598540810850300
Fernando, Y., Chidambaram, R. R., & Wahyuni-TD, I. S. (2018). The impact of Big Data analytics and data security practices on service supply chain performance. Benchmarking: An International Journal 25(9). https://doi.org/10.1108/BIJ-07-2017-0194
Fortune Business Insights. (2024). The global retail analytics market is projected to grow from $5.84 billion in 2021 to $18.33 billion in 2028 at a CAGR of 17.7% in the forecast period, 2021-2028. Retrieved from https://www.fortunebusinessinsights.com/industry-reports/retail-analytics-market-101273
Gakombe, B. R., Musengi, P. S., & Sithole, S. L. (2021). The Role of Descriptive Analytics in Improving Supply Chain Performance: Evidence from South African Retailers. Journal of African Business, 22(4), 521-542. https://doi.org/10.1080/15228916.2021.1914540
Galbraith, S. (2021). Master of organization design–recognizing patterns from living, breathing organizations. In The Palgrave Handbook of Organizational Change Thinkers (pp. 631-650). Cham: Springer International Publishing. Retrieved from https://doi.org/10.1007/978-3-319-52878-6_39
Ganeshkumar, C., & Khan, A. (2021, January). Mapping of agritech companies in Indian agricultural value chain. In Proceedings of the Second International Conference on Information Management and Machine Intelligence: ICIMMI 2020 (pp. 155-161). Singapore: Springer Singapore. https://doi.org/10.1007/978-981-15-9689-6_18
Ghasemi, A., & Zahediasl, S. (2012). Normality tests for statistical analysis: a guide for non-statisticians. International journal of endocrinology and metabolism, 10(2), 486. https://doi.org/10.5812/ijem.3505
Gopal, P. R. C., Rana, N. P., Krishna, T. V., & Ramkumar, M. (2022). Impact of big data analytics on supply chain performance: an analysis of influencing factors. Annals of Operations Research, 1-29. https://doi.org/10.1007/s10479-022-04749-6
Grant T. (2017). Locking down the value of data. Retrieved from https://www.grantthornton.com.ph/globalassets/global-insights-do-not-edit/2017/04.12.2017-locking-down-the-value-of-data/locking-down-the-value-of-data-report_final_online.pdf
Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S. F., Childe, S. J., Hazen, B., & Akter, S. (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, 308-317. https://doi.org/10.1016/j.jbusres.2016.08.004
Gupta, S., Drave, V. A., Dwivedi, Y. K., Baabdullah, A. M., & Ismagilova, E. (2020). Achieving superior organizational performance via big data predictive analytics: A dynamic capability view. Industrial Marketing Management, 90, 581-592. https://doi.org/10.1016/j.indmarman.2019.11.009
Haußmann, C., Dwivedi, Y. K., Venkitachalam, K., & Williams, M. D. (2012). A summary and review of Galbraith’s organizational information processing theory. Information Systems Theory, 71-93. https://doi.org/10.1007/978-1-4419-9707-4_5
He, J., & Zhou, Y. (2019). Diagnostic analytics for supply chain risk management: A case study of a Chinese high-tech manufacturing company. Journal of Cleaner Production, 229, 259-272.
Hill, K. (2012). How Target Figured Out a Teen Girl Was Pregnant Before Her Father Did. Forbes. Retrieved from https://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/?sh=1337c7f16668
Hofmann, E., & Rutschmann, E. (2018). Big data analytics and demand forecasting in supply chains: a conceptual analysis. The international journal of logistics management, 29(2), 739-766. https://doi.org/10.1108/IJLM-04-2017-0088
Hu, Q., & Zuo, Y. (2021). Descriptive analytics for retail supply chain performance: A literature review. Journal of Retailing and Consumer Services, 61, 102524.
Iliyasu, R., & Etikan, I. (2021). Comparison of quota sampling and stratified random sampling. Biom. Biostat. Int. J. Rev, 10(1), 24-27. Retrieved from https://doi.org/10.15406/bbij.2021.10.00326
Ismail, N., Kinchin, G., & Edwards, J. A. (2018). Pilot study, Does it really matter? Learning lessons from conducting a pilot study for a qualitative PhD thesis. International Journal of Social Science Research, 6(1), 1-17. https://doi.org/10.5296/ijssr.v6i1.11720
Jeble, S., Dubey, R., Childe, S. J., Papadopoulos, T., Roubaud, D., & Prakash, A. (2018). Impact of big data and predictive analytics capability on supply chain sustainability. The International Journal of Logistics Management, 29(2), 513-538.
Jeng, C. C. (2023). Why a variance inflation factor of 10 is not an ideal cutoff for multicollinearity diagnostics. Journal of Education Studies, 57(2), 67-93.
Jeong, Y., & Jung, M. J. (2016). Application and interpretation of hierarchical multiple regression. Orthopaedic Nursing, 35(5), 338-341. Retrieved from https://doi.org/10.1097/nor.0000000000000279
Juma, L., & Kilani, S. (2022). Adoption enablers of big data analytics in supply chain management practices: the moderating role of innovation culture. Uncertain Supply Chain Management, 10(3), 711-720. https://doi.org/10.5267/j.uscm.2022.5.004
Kagechu, I. (2018). Impact of Big Data on Organizational Performance: A Case of Listed Commercial Banks in Kenya. Master’s Thesis, United States International University, Nairobi, Kenya. Retrieved from https://erepo.usiu.ac.ke/handle/11732/4550;jsessionid=BD54169E28EA801F37BEEC13F6B3E1D0
Kajwang, B. (2022). Implications for big data and analytics on underwriting practices in insurance sector. American Journal of Data, Information and Knowledge Management, 3(1), 1-11. https://doi.org/10.47672/ajdikm.1135
Kanali, N. (2021). Disrupting the supply chain with better data analysis, SAS. Retrieved from https://africabusinesscommunities.com/tech/tech-news/south-africa-disrupting-the-supply-chain-with-better-data-analysis-sas/
Kang, H. (2013). A guide on the use of factor analysis in the assessment of construct validity. Journal of Korean Academy of Nursing, 43(5), 587-594. https://doi.org/10.4040/jkan.2013.43.5.587
Karim, A. (2021). Enhancing supply chain resilience through prescriptive analytics: An empirical study in the Egyptian pharmaceutical industry. Journal of Manufacturing Technology Management, 32(2), 398-413.
Kaur, P., Stoltzfus, J., & Yellapu, V. (2018). Descriptive statistics. International Journal of Academic Medicine, 4(1), 60. Retrieved from https://doi.org/10.4103/IJAM.IJAM_7_18
Kenya National Bureau of Statistics. (2019). 2019 Kenya Population and Housing Census Volume I: Population by County and Sub-County.
Khan, S., & Burney, A. (2020). Prescriptive analytics for supply chain optimization: A literature review and future research directions. Computers & Industrial Engineering, 141. https://doi.org/10.1016/j.cie.2019.106316
Kibe, L. (2018). The Influence of Big Data Analytics on Organizational performance: A Comparative Study of the Technical University of Kenya and Strathmore University. Master’s Thesis, Technical University of Kenya, Nairobi, Kenya. Retrieved from https://www.researchgate.net/publication/358396020_THE_INFLUENCE_OF_BIG_DATA_ANALYTICS_ON_ORGANISATIONAL_PERFORMANCE_A_COMPARATIVE_STUDY_OF_THE
Kim, J., & Lee, J. (2019). Descriptive analytics for retail supply chain management: A case study. Sustainability, 11(4), 1014.
Kitali, A. E., & Wafula, W. N. (2019). Big data analytics for supply chain management in Africa: An exploratory case study of Kenya. International Journal of Supply Chain Management, 8(3), 403-412.
Kitchin, R., & McArdle, G. (2016). What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets. Big Data & Society, 3(1). Retrieved from https://doi.org/10.1177/2053951716631130
Kohan, S. (2021). Amazon’s Net Profit Soars 84% With Sales Hitting $386 Billion. Forbes. Retrieved from https://www.forbes.com/sites/shelleykohan/2021/02/02/amazons-net-profit-soars-84-with-sales-hitting-386-billion/?sh=79ea81bb1334
Kothari, C. R. (2004). Research methodology: Methods and techniques. New Age International. Retrieved from https://books.google.ro/books/about/Research_Methodology.html?id=8c6gkbKi-F4C&redir_esc=y
Kotzé, T., Kotzé, L., & Nel, H. (2019). Assessing the impact of predictive analytics on supply chain performance in a South African context. Journal of Transport and Supply Chain Management, 13, 1-9. https://doi.org/10.4102/jtscm.v13i0.391
Kourentzes, N., Barrow, D., & Crone, S. F. (2019). Neural network ensemble operators for time series forecasting: An empirical evaluation. European Journal of Operational Research, 272(3), 1107-1118. https://doi.org/10.1016/j.ejor.2018.06.054
Kumar, S., Teichman, M., & Timpernagel, T. (2016). Examining the role of organizational culture and leadership in supply chain performance. International Journal of Physical Distribution & Logistics Management, 46(10), 965-990.
Kusi-Sarpong, S., Orji, I. J., Gupta, H., & Kunc, M. (2021). Risks associated with the implementation of big data analytics in sustainable supply chains. Omega, 105. Retrieved from https://doi.org/10.1016/j.omega.2021.102502
Lala, G. (2014). The emergence and development of the technology acceptance model (TAM). Marketing from Information to Decision, 7, 149-160. Retrieved from https://www.semanticscholar.org/paper/The-Emergence-and-Development-of-the-Technology-Lala/312b5fcea70973c49868c55b787ad7f39975f072
Lambert, D. M., & Cooper, M. C. (2000). Issues in supply chain management. Industrial marketing management, 29(1), 65-83. https://doi.org/10.1016/S0019-8501(99)00113-3
Lavastre, O., Gunasekaran, A., & Spalanzani, A. (2014). Effect of firm characteristics, supplier relationships and techniques used on supply chain risk management (SCRM): an empirical investigation on French industrial firms. International Journal of Production Research, 52(11), 3381-3403. https://doi.org/10.1080/00207543.2013.878057#.U9AFNLHZi4Q
Lewis, M. (2007). Stepwise versus Hierarchical Regression: Pros and Cons. Conference: Southwest Educational Research Association 2007 Annual Meeting. Retrieved from https://www.researchgate.net/publication/235464734_Stepwise_versus_hierarchical_regression_Pros_and_cons
Li, D., & Wang, X. (2017). Dynamic supply chain decisions based on networked sensor data: an application in the chilled food retail chain. International Journal of Production Research, 55(17), 5127-5141. https://doi.org/10.1080/00207543.2015.1047976
Lim, M. K., & Kim, Y. G. (2019). Predicting supply chain performance using diagnostic analytics. International Journal of Production Economics, 211, 170-182.
Lodemann, S., Lechtenberg, S., Wesendrup, K., Hellingrath, B., Hoberg, K., & Kersten, W. (2022). Supply Chain Analytics: Investigating Literature-Practice Perspectives and Research Opportunities. Logistics Research, 15(1). https://doi.org/10.23773/2022_7
Lounsbury, M., & Boxenbaum, E. (2013). Institutional logics in action. In Institutional logics in action, part A (Vol. 39, pp. 3-22). Emerald Group Publishing Limited. Retrieved from https://www.researchgate.net/publication/316083436_Institutional_Logics_in_Action
Lowe, N. K. (2019). What is a pilot study?. Journal of Obstetric, Gynecologic & Neonatal Nursing, 48(2), 117-118. https://doi.org/10.1016/j.jogn.2019.01.005
Madhushani, H. D. P. (2016). Ethical issues in social science research: A review.
Magenda, P. N., & Iravo, M. A. (2014). Factors affecting the adoption of global sourcing: A case study of Tuskys supermarkets, Kenya. IOSR Journal of Business and Management (IOSR-JBM), 16(4), 72-76. https://doi.org/10.9790/487X-16457276
Mahfoudh, I. O. (2013). Effect of selected firm characteristics on financial performance of firms listed in the agricultural sector at the Nairobi securities exchange (Doctoral dissertation, University of Nairobi). Retrieved from https://erepository.uonbi.ac.ke/items/4b00e47e-73b4-4427-8d1d-50e669be501a
Maiti, T., Garg, M., & Kumar, A. (2020). Prescriptive analytics for sustainable supply chain management. Journal of Cleaner Production, 260, 121035.
Marangunić, N., & Granić, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. Universal access in the information society, 14(1), 81-95. https://doi.org/10.1007/s10209-014-0348-1
Marshall, G., & Jonker, L. (2011). An introduction to inferential statistics: A review and practical guide. Radiography, 17(1), e1-e6. Retrieved from https://doi.org/10.1016/j.radi.2009.12.006
Mburu, P. (2023). Report: Kenyans now buy 80pc of the food they eat. Retrieved from https://nation.africa/kenya/business/report-kenyans-now-buy-80pc-of-the-food-they-eat-4285902
McCormick, K., & Salcedo, J. (2017). SPSS statistics for data analysis and visualization. John Wiley & Sons. Retrieved from https://books.google.ro/books/about/SPSS_Statistics_for_Data_Analysis_and_Vi.html?id=kvkwBgAAQBAJ&redir_esc=y
Meyer, J. W., Greenwood, R., & Oliver, C. (2017). Reflections on institutional theories of organizations. In The Sage handbook of organizational institutionalism, 831-852. Retrieved from https://sk.sagepub.com/hnbk/edvol/sage-handbook-of-organizational-institutionalism-2e/chpt/32-reflections-institutional-theories-organizations#_
McLeod, S. (2017). Research methods: Types of research. Simply Psychology. Retrieved from https://www.simplypsychology.org/research-methods.html
Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. Sage. Retrieved from https://books.google.ro/books/about/Qualitative_Data_Analysis.html?id=U4lU_-wJ5QEC&redir_esc=y
Mishra, D., Luo, Z., Hazen, B., Hassini, E., & Foropon, C. (2019). Organizational capabilities that enable big data and predictive analytics diffusion and organizational performance: A resource-based perspective. Management Decision, 57(8), 1734-1755. https://doi.org/10.1108/MD-03-2018-0324
Mugenda, M. (2003). Research Methods, Quantitative and Qualitative Approaches. Nairobi: Acts Press. Retrieved from https://scholar.google.com/citations?view_op=view_citation&hl=en&user=FovF9ccAAAAJ&citation_for_view=FovF9ccAAAAJ:evX43VCCuoAC
Mwaura, J. W., Kibet, L. K., & Gathenya, J. W. (2021). Predictive analytics and its role in supply chain management in Africa: A case study of a Kenyan company. Journal of Transport and Supply Chain Management, 15, 1-12. https://doi.org/10.4102/jtscm.v15i0.575
Nandi, M. L., Nandi, S., Moya, H., & Kaynak, H. (2020). Blockchain technology-enabled supply chain systems and supply chain performance: a resource-based view. Supply Chain Management: An International Journal, 25(6), 841-862.
Nassaji, H. (2015). Qualitative and descriptive research: Data type versus data analysis. Language Teaching Research, 19(2), 129-132. https://doi.org/10.1177/1362168815572747
Nderi, C. G. (2014). Business analytics and performance of commercial banks in Kenya. Doctoral dissertation, University of Nairobi. Retrieved from https://erepository.uonbi.ac.ke/server/api/core/bitstreams/4c73adc2-3a76-42c2-aa53-9b62cb75f252/content
Nemoto, T., & Beglar, D. (2014). Likert-scale questionnaires. In JALT 2013 Conference Proceedings (pp. 1-8). Retrieved from https://jalt-publications.org/sites/default/files/pdf-article/jalt2013_001.pdf
Ocasio, W., Thornton, P. H., & Lounsbury, M. (2017). Advances to the institutional logics’ perspective. The Sage handbook of organizational institutionalism, 2, 509-531. Retrieved from https://experts.illinois.edu/en/publications/advances-to-the-institutional-logics-perspective/
Ojiako, U., Chipulu, M., & Ashleigh, M. (2015). Prescriptive analytics for enhancing supply chain resilience. Journal of Business Research, 68(8), 1742-1751.
Okumu, M. (2021). Value of application of insights from Big Data Analytics on transformation of agriculture: case of farmers subscribed to Mkulima Techie Kenya. Doctoral dissertation, Strathmore University. Retrieved from https://su-plus.strathmore.edu/items/f0f7b395-9b4c-4ed8-88fb-7905142c85bc
Ondieki, M. B. (2017). Effects of Big Data Analytics on Performance: A Case of Selected Telecommunication Firms in Kenya. Doctoral dissertation, United States International University-Africa. Retrieved from https://erepo.usiu.ac.ke/handle/11732/3640
Ongoro, J. K. (2022). Effects of Big Data Analytics on Competitive Advantage: A Case of Safaricom PLC. Doctoral dissertation, United States International University-Africa.
Osanloo, A., & Grant, C. (2016). Understanding, selecting, and integrating a theoretical framework in dissertation research: Creating the blueprint for your “house”. Administrative issues journal: connecting education, practice, and research, 4(2), 7. Retrieved from https://files.eric.ed.gov/fulltext/EJ1058505.pdf
Osborne, J. W., & Waters, E. (2019). Four assumptions of multiple regression that researchers should always test. Practical assessment, research, and evaluation, 8(1), 2. Retrieved from https://www.researchgate.net/publication/234616195_Four_Assumptions_of_Multiple_Regression_That_Researchers_Should_Always_Test
Otieno, R., & Khaemba, D. N. (2018). Diagnostic analytics and performance of food supply chain in Kenya: A case of maize sector. International Journal of Supply Chain Management, 7(5), 172-181.
Owusu, R. K., Dwomoh, E., Appiah, R. C., & Opoku, R. (2020). Impact of predictive analytics on supply chain performance in a developing country context: Evidence from Ghana. International Journal of Logistics Systems and Management, 37(3), 369-388. https://doi.org/10.1504/IJLSM.2020.107973
Owusu, R., Koomson, I., & Donkor-Hyiaman, K. (2020). A prescriptive analytics approach to improve supply chain performance in the Ghanaian oil and gas industry. Journal of Industrial and Production Engineering, 37(7), 546-560.
Park, Y. S., Konge, L., & Artino, A. R. (2020). The positivism paradigm of research. Academic Medicine, 95(5), 690-694. Retrieved from https://doi.org/10.1097/acm.0000000000003093
Pérez-Escamilla, R. (2017). Food security and the 2015–2030 sustainable development goals: From human to planetary health: Perspectives and opinions. Current developments in nutrition, 1(7). https://doi.org/10.3945/cdn.117.000513
Peters, B. G. (2022). Institutional theory. In Ansell, C., Torfing, J., Handbook on theories of governance (pp. 323-335). Edward Elgar Publishing. Retrieved from https://books.google.ro/books/about/Handbook_on_Theories_of_Governance.html?hl=id&id=tElgEAAAQBAJ&redir_esc=y
Queiroz, M. M., & Telles, R. (2018). Big data analytics in supply chain and logistics: an empirical approach. The International Journal of Logistics Management 7(4). https://doi.org/10.1108/IJLM-05-2017-0116
Queiroz, M. M., & Telles, R. (2018). Big data analytics in supply chain and logistics: an empirical approach. The International Journal of Logistics Management 8(2). https://doi.org/10.1108/IJLM-05-2017-0116
Rahman, M. N. (2017, September). Trends and challenges of food retail sector in Saudi Arabia. In Proceedings of 9th European Business Research Conference, Madrid, Spain. Retrieved from https://bit.ly/2HuOUjM
Ramos, E., Coles, P. S., Chavez, M., & Hazen, B. (2022). Measuring agri-food supply chain performance: insights from the Peruvian kiwicha industry. Benchmarking: An International Journal, 29(5), 1484-1512. https://doi.org/10.1108/BIJ-10-2020-0544
Rischke, R., Kimenju, S. C., Klasen, S., & Qaim, M. (2015). Supermarkets and food consumption patterns: The case of small towns in Kenya. Food Policy, 52, 9-21. https://doi.org/10.1016/j.foodpol.2015.02.001
Rozados, I. V., & Tjahjono, B. (2014, December). Big data analytics in supply chain management: Trends and related research. In 6th International Conference on Operations and Supply Chain Management (Vol. 1, p. 13). https://doi.org/10.13140/RG.2.1.4935.2563
RSM. (2020). Harnessing the potential of Africa’s supply chain and logistics sector. Retrieved from https://www.rsm.global/insights/finding-opportunity-change/harnessing-potential-africas-supply-chain-and-logistics-sector
Saunders, M., Lewis, P., & Thornhill, A. (2019). Research methods for business students (8th ed.). Pearson. Retrieved from https://www.researchgate.net/publication/240218229_Research_Methods_for_Business_Students
Sahu, P. K., Pal, S. R., & Das, A. K. (2015). Estimation and inferential statistics. New Delhi: Springer. Retrieved from https://books.google.ro/books/about/Estimation_and_Inferential_Statistics.html?id=X7_fCgAAQBAJ&redir_esc=y
Scott, W. R. (2017). Institutional theory: Onward and upward. In The Sage handbook of organizational institutionalism, 853-869. Retrieved from https://sk.sagepub.com/hnbk/edvol/sage-handbook-of-organizational-institutionalism-2e/chpt/33-institutional-theory-onward-upward
Seyedan, M., & Mafakheri, F. (2020). Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities. Journal of Big Data, 7(1), 1-22. https://doi.org/10.1186/s40537-020-00329-2
Shafritz, J. M., Ott, J. S., & Jang, Y. S. (2015). Classics of organization theory. Cengage Learning. Retrieved from https://books.google.ro/books/about/Classics_of_Organization_Theory.html?id=lOTrBQAAQBAJ&redir_esc=y
Šmaguc, T. (2022). A Synthesis of Overlapping and Divergent Areas of the Resource-Based View of the Firm and Entrepreneurship Theory. UTMS Journal of Economics, 13(1), 1-15. Retrieved from https://www.econstor.eu/bitstream/10419/281907/1/181280007X.pdf
Souza, G. C. (2014). Supply chain analytics. Business Horizons, 57(5), 595-605. https://doi.org/10.1016/j.bushor.2014.06.004
Srinivasan, R., & Swink, M. (2018). An investigation of visibility and flexibility as complements to supply chain analytics: An organizational information processing theory perspective. Production and Operations Management, 27(10), 1849-1867. https://doi.org/10.1111/poms.12746
Stanton, J. L. (2018). A brief history of food retail. British Food Journal, 120(1), 172-180. https://doi.org/10.1108/BFJ-01-2017-0033
Stewart, D., & Klein, S. (2016). The use of theory in research. International journal of clinical pharmacy, 38(3), 615-619. Retrieved from https://pubmed.ncbi.nlm.nih.gov/26511946/
Tachizawa, E. M., & Gimenez, F. A. P. (2016). A descriptive analysis of research on retail supply chain management: A review and future directions. International Journal of Physical Distribution & Logistics Management, 46(9), 886-906.
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach's alpha. International Journal of Medical Education, 2(53). https://doi.org/10.5116/ijme.4dfb.8dfd
The Economist. (2022). Why it costs so much to move goods around Africa? Retrieved from https://www.economist.com/middle-east-and-africa/2022/03/26/why-it-costs-so-much-to-move-goods-around-africa
Turner, P. (2020). Critical values for the Durbin-Watson test in large samples. Applied Economics Letters, 27(18), 1495-1499. https://doi.org/10.1080/13504851.2019.1691711
Twiga Foods. (n.d). 2023 ESG Impact Report. Retrieved from https://twiga.com/wp-content/uploads/2023/10/Twiga-Foods-ESG-Impact-Report-2023.pdf
Twiga.com. (n.d). About us. Retrieved from https://twiga.com/about/
Van der Vorst, J. G., Beulens, A. J., & Schaap, L. (2018). Big data analytics for supply chain management: A literature review and research agenda. International Journal of Production Research, 56(1-2), 396-418.
Wainer, H., & Braun, H. I. (Eds.). (2013). Test validity. Routledge. https://doi.org/10.4324/9780203056905
Waithera, V. (2023). 10 tech startups in Kenya that are focused on improving the food industry. Retrieved from https://techmoran.com/2023/03/27/10-tech-startups-in-kenya-that-are-focused-on-improving-the-food-industry/
Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International journal of production economics, 176, 98-110. https://doi.org/10.1016/j.ijpe.2016.03.014
Wasike, L. (2020). Big Data Analytics and Supply Chain Performance of Network Facilities Providers in Kenya. Master’s Thesis, University of Nairobi. Retrieved from http://erepository.uonbi.ac.ke/bitstream/handle/11295/154686/Wasike_Big%20Data%20Analytics%20and%20Supply%20Chain%20Performance%20of%20Network%20Facilities%20Providers%20in%20Kenya.pdf?sequence=1&isAllowed=y
Wawire, A. K. (2021). Firm Characteristics and Operational Efficiency of Agricultural Firms Listed at Nairobi Securities Exchange in Kenya (Doctoral dissertation, KCA University). Retrieved from https://www.semanticscholar.org/paper/Firm-Characteristics-and-Operational-Efficiency-of-Wawire-Njogo/2c8bf7509b3e7a6e96acc322b8055caa7e564a42
WFP. (2022). Eastern Africa, Supply Chain outcomes in the Food System: Evaluation. Retrieved from https://www.wfp.org/publications/eastern-africa-supply-chain-outcomes-food-system-evaluation
Williams, M. N., Grajales, C. A. G., & Kurkiewicz, D. (2013). Assumptions of multiple regression: Correcting two misconceptions. Practical Assessment, Research, and Evaluation, 18(1), 11. Retrieved from https://files.eric.ed.gov/fulltext/EJ1015680.pdf
Wolf, J. (2014). The relationship between sustainable supply chain management, stakeholder pressure and corporate sustainability performance. Journal of Business Ethics, 119(3), 317-328. Retrieved from https://ideas.repec.org/a/kap/jbuset/v119y2014i3p317-328.html
Won, D., & Chelladurai, P. (2016). Competitive advantage in intercollegiate athletics: Role of intangible resources. PloS one, 11(1). https://doi.org/10.1371/journal.pone.0145782
World Bank. (2022). Total data volume worldwide. Retrieved from https://www.statista.com/statistics/871513/worldwide-data-created/
Wu, W., Ding, J., & Li, Y. (2021). Prescriptive analytics for supply chain risk management: A case study of a pharmaceutical company. International Journal of Production Research, 59(4), 977-993.
Xetova. (2022). Strathmore Business School: Unlocking Africa’s Supply Chain: The Advent of Big Data in Supply Chain Management. Retrieved from https://xetova.com/strathmore-business-school-unlocking-africas-supply-chain-the-advent-of-big-data-in-supply-chain-management/
Yaghmaie, F. J. A. M. (2003). Content validity and its estimation. Journal of medical education, 3(1). Retrieved from https://www.researchgate.net/publication/277034169_Content_validity_and_its_estimation
Yang, K., Tu, J., & Chen, T. (2019). Homoscedasticity: an overlooked critical assumption for linear regression. General psychiatry, 32(5). https://doi.org/10.1136/gpsych-2019-100148
Yap, B. W., & Sim, C. H. (2011). Comparisons of various types of normality tests. Journal of Statistical Computation and Simulation, 81(12), 2141-2155. https://doi.org/10.1080/00949655.2010.520163
Zahid, M. J. A., Ashraf, M. M., & Malik, B. T. (2013). An impact assessment of information and communication technology for disabled people through technology acceptance model in Bangladesh. International Journal of Services Technology and Management, 19(1-3), 43-62. https://doi.org/10.1504/IJSTM.2013.054197
DOI: http://dx.doi.org/10.46827/ejefr.v10i5.2287
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