The impact of Artificial Intelligence on E-commerce supply chain sector in achieving cost efficiency and economic growth: A business and economics perspective

Abstract

The study aims to find how much cost effectiveness is achieved when e-commerce supply chain operations use automation and AI technologies. therefore,ing technology of AI and its models have essentially proved to be impactful in making data driven decisions by the organisations gaining leverage in productivity and profit sustainability in a way that the companies achieve competitive advantage therefore creating sustainable value propositions from business and economics perspective. Aim/Purpose The aim of the paper is to explore the positive relationship between AI technologies and productivity levels in the e-commerce supply chain sector through achieving cost effectiveness and economic growth. Methodology/Approach The study focussed on reviewing literature on how Artificial Intelligence driven technology can optimise various supply chain functions within e-commerce sector. Solow-Swan growth model has been applied to investigate the value AI creates in utilising capital, labour input to achieve output growth. Positivist approach that allowed for objective observation and independent conclusions has been adopted. For primary data, quantitative methodology is used through survey questionnaires to gather data from a sample of 206 employees, managers, data analysts in e-commerce supply chain sectors. Findings The findings from the secondary sources inform that the use of AI and automation in the e-commerce industry leads to a high rate of productivity in terms of reducing costs and promoting economic growth. The primary research methods, through survey questionnaires collects real-time data that helps achieve quantifiable and measurable values to conclude that AI-led technology can increase productivity and competitive advantage as it saves cost while increasing productivity and overall economic input. Descriptive statistics of measures of central tendency were used to present findings in simpler, presentable way followed by interpretations of the data in percentages. Practical implications Managers and decision-making directorial board members have important lessons to learn from these findings as the quantifiable values may give them an insight into how much capital investment should be allocated for AI technologies and how predictive analytics and data analysis can accelerate their service towards becoming customer centric. Significant strategic planning and implementation of resource management can lead to higher rates of productivity profitability and eventually higher economic growth.