Elevating Self-Service Business Intelligence through AI and IoT in Sub-Saharan Africa

The emergence of Big Data, Artificial Intelligence (AI), and the Internet of Things (IoT) has become a critical component in enhancing competitive advantage. In the contemporary landscape, organisations must use business information (BI) to facilitate more astute and expedient decision-making; nevertheless, this practice is less prevalent among African enterprises. This chapter analyses the impact of self-service business intelligence on customer satisfaction, focusing on system quality, user training efficacy, data accessibility, and the moderating influence of non-technical users. It seeks to elucidate the links among these factors and their impact on customer experiences in enterprises using self-service business intelligence technologies inside sustainable manufacturing firms in Nigeria. The study employs a survey research design which is quantitative in nature. The Research Advisor Table, was used to determine the sample size of 378. To mitigate the problem of non-response, suitable actions were implemented, resulting in the incorporation of extra 113 respondents, constituting 30% of the initial sample. The modification yielded a final sample size of 491 from various industries including retail, finance, manufacturing, and healthcare. From the 480 copies of the questionnaire were filled and returned for analysis, with a response rate of 97.76%. Relationships between constructs, the moderating effect of non-technical user independence and customer satisfaction were investigated using regression analysis. Findings show that customer satisfaction is greatly impacted by self-service business intelligence (system quality, user training effectiveness, and data accessibility) of sustainable manufacturing companies in Lagos State Nigeria. The inverse relationship between the moderating role of non-technical user independence shows that a greater degree of independence combined with less support may result in mistakes that reduce customer satisfaction. The chapter sheds light on the intricacy of self-service business intelligence and customer satisfaction with a focus on independent non-technical user. It provides practitioners and scholars looking to maximise the usage of self-service business intelligence with enlightening inputs. The study&s;s practical application indicates that in order to assist non-technical end users in handling data responsibly, organisations should invest in robust self-service business intelligence systems, extensive training programs, and easy access to data in addition to support mechanisms.