Examining the effect of user satisfaction and business intelligence system usage on individual job performance

Business intelligence systems are in widespread use today due to the many business benefits. Users are one of the key stakeholders in the business intelligence process. For optimal system adaptation, the user should be able to interact with the application in order to improve its capacity to contribute to decision-making. For the business intelligence process itself to be effective, it is necessary to define the user needs regardless of the type of work they do. If the user is satisfied and thinks that the system improves his/her performance or the quality of decisions made, they will want to use it even more. System usage has sometimes been viewed as a direct reflection of system performance; however, this is difficult to define in organizations where system usage is mandatory. Business intelligence systems are especially mandatory to use, as they are used in large organizations and require greater investment than other systems. This is why it is important to investigate the nature of system usage and its impact on individual performance. This research model deals with determinants that represent dimensions of the information system's success theory. Those determinants are: user satisfaction, intention to use, system usage, and individual performance. Obtained results show that increased user satisfaction and intention to use, lead to increased system usage and that both the increase in user satisfaction and system usage lead to a rise in individual user performance.

Measures of Engagement with mHealth Interventions in Patients with Heart Failure: A Scoping Review (Preprint)

BACKGROUND The computing and communication features of mobile devices are increasingly leveraged in mHealth interventions to provide comprehensive and tailored support that may have positive outcomes in patients with heart failure (HF). However, examination of mHealth intervention effectiveness has provided mixed findings. Considering that patient engagement is a prerequisite for the effectiveness of interventions, understanding how patients engage with mHealth interventions, and the effects of patient engagement on HF outcomes may explain the mixed findings. OBJECTIVE Our aim was to synthesize current evidence on measures of patient engagement with mHealth interventions, and the effects of engagement on HF outcomes METHODS A comprehensive search of the literature was conducted in 7 databases for relevant studies published in the English Language from 2009 to September 2021. Descriptive characteristics of studies were reported. Content analysis was conducted to identify themes that described patient engagement with mHealth in the qualitative studies included in the review. RESULTS We synthesized 32 studies that operationalized engagement with mHealth interventions in 4771 patients with HF (67.9% male), ranging from a sample of 7 to 1571, with a median of 53.3 patients. Patient engagement with mHealth interventions was measured only quantitatively based on system usage data (71.8%, 23/32), only qualitatively based on data from semi-structured interviews and focus groups (6.3%, 2/32), and by a combination of both quantitative and qualitative data (21.9%, 7/32). System usage data were evaluated using 6 metrics of engagement: (1) number of physiological parameters transmitted (63.3%, 19/30); (2) number of HF questionnaires completed (6.7%, 2/30); (3) numbers of logins (13.3%, 4/30); (4) number of short message service (SMS) responses (3.3%, 1/30); (5) time spent (16.7%, 5/30); (6) number of features accessed/screen viewed (9.5%, 4/30). There was a lack of consistency in how system usage metrics were reported across the studies. Eighty percent of the studies reported only the descriptive characteristics of the system usage data. Emotional, cognitive, and behavioral domains of patient engagement were identified in qualitative studies. Patient engagement levels ranged from 45% to 100% and decreased over time. The effects of engagement on HF knowledge, self-care, exercise adherence, and HF hospitalizations were inconclusive. CONCLUSIONS The operational definitions of patient engagement with mHealth interventions are underreported and lack consistency. The application of inferential analytical methods to engagement data is extremely limited. More research focused on developing optimal and standardized measures of patient engagement that may be applied across different study designs is warranted.

Examining Information System’s Usage and Performance Indicators Using Technology Utilization, Satisfaction and Performance

While the development of information systems in workplaces with the aim of achieving cost-effectiveness, efficiency and quality of service delivery remain sacrosanct, issues of effective utilization and its resultant implications on organizational performance remain critical from one context to another. Unfortunately, few studies had considered focusing on these causal relationships among information system deployments in the construction industry especially in developing countries like Nigeria. This work modelled the interactions causal relationships associated with task technology fit, system usage and performance variables using the TUSPEM model. Through convenience and stratified sampling techniques, the views of 136 senior staff including top level management staff, sectional heads and other senior staff of a construction firm in Nigeria were sought. Smart PLS structural equation modeling software was used for the analysis of the dataset. The result showed significant relationships between causal variables in the TUSPEM model such as Application utilization to performance (t-value 2.44, P< 0.02), utilization to user satisfaction (t-value 2.87, P< 0.01). TTF to performance (t-value 2.86, P< 0.06), satisfaction (t-value 4.40, P< 0.00), User attitude to utilization (t-value 5.40, P< 0.00). Computer 2self-efficacy to utilization (t-value 4.47, P< 0.00). User satisfaction to performance (t-value 2.47, P< 0.01). Critical appraisal and integration of quality feedback on information system usage and its resultant effects on the numerous information systems being deployed must not be sideline if the sustainability of information system is anything to go by. Other implications are discussed.