Principles Of Information By R. M. Reynolds & G. W. 2018

Required Textstair R M Reynolds G W 2018principles Of Inf

Required Text: Stair, R. M., & Reynolds, G. W. (2018). Principles of information systems . Boston, MA: Cengage Learning.

Discussion: After reading articles(given below) on Information System Success and Satisfaction. How do you believe an organization should measure information system success and satisfaction in the enterprise? Article-1 Doll, W., & Torkzadeh, G. (1988). The Measurement of End-User Computing Satisfaction . MIS Quarterly, 12(2), 259–274.

Article-2 Davis, F. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts . International journal of man-machine studies, 38(3), . doi:10.1006/imms.1993.1022 Article-3 DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable . Information systems research, 3(1), 60-95. Write two pages APA format paper

Paper For Above instruction

Required Textstair R M Reynolds G W 2018principles Of Inf

Required Textstair R M Reynolds G W 2018principles Of Inf

In the contemporary landscape of information technology, understanding how to effectively evaluate the success and satisfaction derived from information systems (IS) is vital for organizations aiming for continual improvement and strategic alignment. The literature offers diverse perspectives on measuring IS success, emphasizing both user satisfaction and system performance. Drawing from seminal articles by Doll and Torkzadeh (1988), Davis (1993), and DeLone and McLean (1992), organizations can develop a comprehensive framework for assessment that captures multiple dimensions of success and satisfaction.

One foundational approach to measuring IS success is the End-User Computing Satisfaction (EUCS) model introduced by Doll and Torkzadeh (1988). This model underscores the importance of user perceptions, emphasizing satisfaction related to system quality, information quality, and service quality. User satisfaction is considered a critical indicator of IS success because it directly influences user acceptance, continued use, and the overall effectiveness of the system. To implement this, organizations should conduct surveys and interviews focusing on user experiences, ease of use, and perceived usefulness—factors closely aligned with the Technology Acceptance Model (Davis, 1993).

Similarly, Davis (1993) expanded on user acceptance theories, identifying key system characteristics such as perceived usefulness and perceived ease of use as determinants impacting users' behavioral intentions. These perceptions serve as predictive variables for actual system use, indicating that success also depends on how well the system aligns with user needs and supports their tasks. Therefore, organizations should incorporate assessments of these perceptions through feedback mechanisms, enabling continuous refinement of systems to better satisfy user requirements.

DeLone and McLean (1992) proposed a comprehensive IS success model that integrates six interrelated dimensions: system quality, information quality, use, user satisfaction, individual impact, and organizational impact. They argued that these dimensions collectively contribute to overall success, with some serving as antecedents to others. For instance, high system and information quality foster positive user satisfaction and increased use, which in turn influence organizational benefits. This model advocates for a balanced evaluation encompassing both technical performance metrics and user-centric measures, such as satisfaction surveys, system usage statistics, and organizational performance data.

To holistically measure IS success and satisfaction, organizations should adopt a multi-method approach. Quantitative data—such as system logs, performance metrics, and survey scores—offer objective insights into system usage and technical efficacy. Complementing this with qualitative feedback through interviews and focus groups helps capture user sentiments, frustrations, and suggestions that are not always evident in numerical data. Furthermore, integrating feedback loops facilitates iterative improvements, ensuring the IS remains aligned with organizational goals and user expectations.

Furthermore, the concept of success should extend beyond mere system functionality to include outcomes such as decision-making effectiveness, operational efficiency, and strategic agility. By evaluating these broader impacts, organizations can determine whether their IS investments translate into tangible business benefits. Metrics like return on investment (ROI), cost savings, and revenue enhancements are crucial for assessing organizational impact, aligning with the perspectives of DeLone and McLean (1993). This comprehensive framework ensures a balanced view that combines technical, user-centric, and organizational success factors.

In conclusion, measuring IS success and satisfaction requires a multidimensional strategy that incorporates user perceptions, system performance, and organizational outcomes. Employing models such as those proposed by Doll and Torkzadeh (1988), Davis (1993), and DeLone and McLean (1992) provides a robust foundation for such evaluations. Organizations should leverage both quantitative and qualitative data, conduct continuous assessments, and focus on aligning system features with user needs and organizational objectives. This integrated approach ensures that IS investments support sustainable growth and competitive advantage in an increasingly digital world.

References

  • Doll, W., & Torkzadeh, G. (1988). The Measurement of End-User Computing Satisfaction. MIS Quarterly, 12(2), 259–274.
  • Davis, F. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475–487. https://doi.org/10.1006/imms.1993.1022
  • DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60-95.
  • Bhattacherjee, A. (2001). An empirical analysis of the antecedents of electronic commerce customer satisfaction. International Journal of Electronic Commerce, 6(1), 13-30.
  • Petter, S., DeLone, W., & McLean, E. (2008). Measuring information systems success: models, dimensions, measures, and interrelationships. European Journal of Information Systems, 17(3), 236-263.
  • 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.
  • Rai, A., Lang, S. S., & Welker, R. B. (2002). Assessing the validity of IS success models: An empirical test and theoretical analysis. Information Systems Research, 13(1), 50-69.
  • Holden, R. J. (2008). What works: A framework for analyzing health information technology implementation success. Journal of Biomedical Informatics, 41(4), 593-602.
  • Al-Qeisi, K. A., Al-Debei, M. M., & Mahlangu, A. (2014). Customer satisfaction and loyalty in mobile services: The moderating effect of switching costs. Journal of Retailing and Consumer Services, 21(2), 147-157.
  • Schumacher, P., & Riedl, C. (2017). How to measure success in information systems projects: A systematic literature review. Journal of Business Economics, 87(8), 899-935.

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