Published July 30, 2026 | Version v1
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Automation of Reporting Tasks and Its Impact on Bandwidth for Customer Experience Managers

Description

Organizations increasingly automate reporting to reduce the manual effort of collecting, verifying, and circulating operational data, a burden that falls heavily on Customer Experience (CX) managers in Software-as-a-Service firms. This study examines whether employee perceptions of automation across five dimensions - Productivity/Time Savings, Ease of Use, Speed & Timeliness, Quality & Error Reduction, and Trust & Satisfaction - predict their overall perception that automation improves operational efficiency and releases bandwidth for higher-value work. Data were collected from 84 working professionals using a 20-item, five-point Likert-scale questionnaire administered through Google Forms. The instrument demonstrated excellent reliability (Cronbach's alpha = 0.918). Descriptive statistics, Pearson correlation, and multiple linear regression were used for analysis, with regression assumptions (normality, multicollinearity, homoscedasticity, autocorrelation) verified. All five constructs correlated positively and significantly with the overall outcome (r = 0.498-0.646), and together explained 50.3% of its variance (R² = 0.503). Speed & Timeliness (β = 0.526), Productivity/Time Savings (β = 0.450), and Ease of Use (β = 0.413) emerged as the strongest predictors. Findings suggest that organizations seeking to maximize perceived bandwidth release should prioritize automating high-frequency, time-consuming tasks and keep interfaces simple, rather than investing primarily in trust-building initiatives.

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Dates

Submitted
2026-07-31
More recently, automation, whether through scripting tools, process-flow automation, artificial intelligence, or business intelligence (BI) platforms, has begun to reshape how reports are generated. These technologies collect and present data both quickly and reliably. By automating report generation, organizations stand to gain considerably in terms of efficiency, since the process saves time and effort, improves the accuracy of the output, and speeds up how quickly information reaches decision-makers. This is especially relevant for Software-as-a-Service (SaaS) companies, many of which must handle substantial volumes of data on an ongoing basis. These companies routinely track subscription metrics, customer behavior, service operations, support requests, renewals, and refunds, all of which fall under the broader umbrella of key performance indicators (KPIs). Given that Customer Experience (CX) managers are typically the ones responsible for this kind of analysis, manually compiling reports becomes both cumbersome and time-intensive, ultimately preventing organizations from reaching their desired levels of efficiency.

References

  • 1. Bardmann, M., et al. (2023). Employee acceptance and resistance to process automation: An empirical study. Journal of Business Research. 2. Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company. 3. Cobelli, N., Cassia, F., & Donvito, R. (2023). Technology acceptance in professional settings: The relative role of ease of use. Technological Forecasting and Social Change. 4. Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116. 5. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. 6. Gomes, C., & Seruca, I. (2023). Robotic process automation adoption: Drivers of acceptance and resistance. Business Process Management Journal. 7. Hakimi, N., Jaafar, M., Mohamad, D., & Omar, S. (2024). Unified Theory of Acceptance and Use of Technology (UTAUT) applied in higher education research: A systematic literature review and bibliometric analysis. 8. Hammer, M., & Champy, J. (1993). Reengineering the corporation: A manifesto for business revolution. Harper Business. 9. Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46(1), 50-80. 10. Muir, B. M. (1994). Trust in automation: Part I. Theoretical issues in the study of trust and human intervention in automated systems. Ergonomics, 37(11), 1905-1922. 11. Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187-192. 12. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12-40. 13. Peng, S., Kalliamvakou, E., Cihon, P., & Demirer, M. (2023). The impact of AI on developer productivity: Evidence from GitHub Copilot. arXiv preprint. 14. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. 15. Xue, L., Rashid, A. M., & Ouyang, S. (2024). The unified theory of acceptance and use of technology (UTAUT): A systematic review of empirical research, 1-25.