DATASET FOR DIGITAL TRANSFORMATION TECHNOLOGIES AND CUSTOMER EXPERIENCE OF SELECTED PRIVATE UNIVERSITIES IN OGUN STATE, NIGERIA
Description
The study examined whether digital transformation technologies significantly influence customer experience in selected private universities in Ogun State, Nigeria. The research hypothesised that Artificial Intelligence (AI), Cloud Computing (CC), Mobile Technologies (MT), and Cybersecurity (CS) have no significant effects on key dimensions of customer experience, including access to learning resources, research skill development, internet connectivity experience, first-contact resolution, and overall customer experience. A quantitative, cross-sectional survey design was adopted. Data were collected using a structured questionnaire administered to 500 respondents, of which 441 valid responses were obtained, representing an 88.2% response rate. The data were analysed using SPSS Version 27, employing descriptive statistics and multiple linear regression at a 5% significance level. The descriptive results indicated generally positive perceptions of digital transformation, with a grand mean of 5.26. Respondents also reported relatively favourable experiences regarding access to learning resources (mean = 4.91), research skill development (mean = 5.05), internet connectivity experience (mean = 4.29), and first-contact resolution (approximately 5.05). The regression results showed that AI, CC, and CS significantly and positively predicted access to learning resources. Together, the technologies explained approximately 39.5% of the variance in this outcome. Similarly, AI, CC, and CS significantly predicted research skill development, explaining approximately 41.3% of its variance. Mobile technologies did not have a statistically significant effect on either outcome. For internet connectivity experience, cybersecurity was the only significant predictor (β = .680, p < .001), while AI, cloud computing, and mobile technologies were not significant. The model explained 15.8% of the variance, suggesting that other factors beyond the technologies examined also contribute substantially to students' connectivity experiences. The strongest overall predictive relationship was observed for first-contact resolution. AI, cloud computing, and cybersecurity had significant positive effects, while mobile technologies had a significant negative effect (β = −.156, p = .018). The model demonstrated a strong overall fit (R = .779; F(4,436) = 167.551, p < .001). Overall, the findings indicate that digital transformation technologies can substantially enhance customer experience in private universities, particularly through AI, cloud computing, and cybersecurity. Cybersecurity emerged as an especially influential factor across several outcomes, whereas mobile technology produced inconsistent effects. The findings suggest that universities should adopt an integrated digital transformation strategy while paying particular attention to cybersecurity, digital infrastructure, and the effective implementation of technology-enabled services.
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Steps to reproduce
Define the research variables: Identify the independent variables—Artificial Intelligence (AI), Cloud Computing (CC), Mobile Technologies (MT), and Cybersecurity (CS)—and the dependent variables representing customer experience. Develop the research instrument: Use a structured questionnaire containing items measuring respondents’ perceptions of digital transformation technologies and customer experience dimensions. Select the study sample: Administer the questionnaire to the target population of respondents from selected private universities in Ogun State, Nigeria. Collect the data: Distribute 500 questionnaires and retain the 441 valid responses obtained, representing an 88.2% response rate. Prepare the dataset: Code and enter the valid questionnaire responses into SPSS Version 27, ensuring that variables and response categories correspond to the questionnaire items. Conduct descriptive analysis: Calculate frequencies, means, and other descriptive statistics to establish respondents’ perceptions of digital transformation technologies and the measured customer-experience dimensions. Test the hypotheses: Apply multiple linear regression at the 5% significance level to determine the individual and combined effects of AI, CC, MT, and CS on access to learning resources, research skill development, internet connectivity experience, first-contact resolution, and overall customer experience. Interpret the results: Examine regression coefficients (β), t-values, p-values, R, adjusted R², and F-statistics. A predictor is considered statistically significant when its p-value is below 0.05. Draw conclusions: Compare the statistical results with the stated null hypotheses and use the findings to explain how digital transformation technologies influence customer experience in the selected private universities.
Institutions
- Babcock UniversityOyo State, Ibadan