An Investigation of Patients’ Decisions to Use eHealth

eHealth service has received increasing attention. Patients can consult online doctors via the Internet, and then physically visit the doctors for further diagnosis and treatments. Although extant research has focused on the adoption of eHealth services, the decision-making process from online to offline health services still remains unclear. This study aims to examine patients’ decisions to use online and offline health services by integrating the extended valence framework and the halo effect. By analyzing 221 samples with online consultation experiences, the results show that trust significantly influences perceived benefits and perceived risks, while trust, perceived benefits, and perceived risks significantly influence the intention to consult. The intention to consult positively influences the intention to visit. Considering the moderating effects of payment types, the influence of perceived risks on the intention to consult is larger for the free group than for the paid group. The findings are useful to better understand patients’ decisions to use eHealth.

Consumer Engagement through Conditional Promotions

This paper investigates consumers' response to conditional promotions (CP) offered in an offline retail store. Using qualitative research inquiry, we decipher the consumer decision-making process by finding the linkages between 'pre-cart' and the 'post-cart' add-on purchases. Thematic analysis of qualitative data (focus groups and personal interviews) resulted in four themes, i.e. 'Criticality of Product Utility,' 'Mode of Payments,' 'Loss Aversion by Consumers,' and 'Inability to Think Out-of-Box by the Consumers.' We add value to the existing marketing literature by finding the relationship between products purchased in 'pre-cart', i.e., without the knowledge of CP and 'post-cart', defined as add-on products added to the cart to avail the CP offer while purchasing in an offline retail store. Further, we find that consumers' willingness to avail CP varies with different relative distances from the target purchase cart value (high vs. low) and mode of payments (cash vs. digital). We discuss the theoretical and managerial implications of the research.

Vibration Edge Computing in Maritime IoT

IoT and the Cloud are among the most disruptive changes in the way we use data today. These changes have not significantly influenced practices in condition monitoring for shipping. This is partly due to the cost of continuous data transmission. Several vessels are already equipped with a network of sensors. However, continuous monitoring is often not utilised and onshore visibility is obscured. Edge computing is a promising solution but there is a challenge sustaining the required accuracy for predictive maintenance. We investigate the use of IoT systems and Edge computing, evaluating the impact of the proposed solution on the decision making process. Data from a sensor and the NASA-IMS open repository were used to show the effectiveness of the proposed system and to evaluate it in a realistic maritime application. The results demonstrate our real-time dynamic intelligent reduction of transmitted data volume by without sacrificing specificity or sensitivity in decision making. The output of the Decision Support System fully corresponds to the monitored system's actual operating condition and the output when the raw data are used instead. The results demonstrate that the proposed more efficient approach is just as effective for the decision making process.

The Footprint of Factorization Models and Their Applications in Collaborative Filtering

Factorization models have been successfully applied to the recommendation problems and have significant impact to both academia and industries in the field of Collaborative Filtering ( CF ). However, the intermediate data generated in factorization models’ decision making process (or training process , footprint ) have been overlooked even though they may provide rich information to further improve recommendations. In this article, we introduce the concept of Convergence Pattern, which records how ratings are learned step-by-step in factorization models in the field of CF. We show that the concept of Convergence Patternexists in both the model perspective (e.g., classical Matrix Factorization ( MF ) and deep-learning factorization) and the training (learning) perspective (e.g., stochastic gradient descent ( SGD ), alternating least squares ( ALS ), and Markov Chain Monte Carlo ( MCMC )). By utilizing the Convergence Pattern, we propose a prediction model to estimate the prediction reliability of missing ratings and then improve the quality of recommendations. Two applications have been investigated: (1) how to evaluate the reliability of predicted missing ratings and thus recommend those ratings with high reliability. (2) How to explore the estimated reliability to adjust the predicted ratings to further improve the predication accuracy. Extensive experiments have been conducted on several benchmark datasets on three recommendation tasks: decision-aware recommendation, rating predicted, and Top- N recommendation. The experiment results have verified the effectiveness of the proposed methods in various aspects.

Improving the quality of strategic decision-making process in universities through employing expert systems: A case study from a developing country

Strategic decisions represent the fundamental core of the strategic planning process and strategic management in universities and they are essential in shaping the universities' policies and achieving their strategic goals. Without those strategic decisions, the universities stand unable to achieve their strategic goals and mission; therefore, specialists realized the critical importance of improving the quality of strategic decision-making in the current complex fast-changing environment that its dynamism continuously increases and which is based on the use of cutting-edge information and communications technology (ICT). Undoubtedly strategic decision-making process requires processing a huge amount of information with different robust smart methods and the extensive use of experts knowledge. There are many discussions about the uses and applications of expert systems (ESs), which are evolving rapidly in solving real problems in many fields that require experienced experts with deep sound experiences, and despite these many applications in many different fields and domains. Literature reveals that there is a scarcity of scientific research on how to employ expert systems to raise the quality of strategic decision-making processes in universities. Thus the purpose of the research is to fill this research gap by investigating how expert systems will enhance the quality of the strategic decision-making process in universities. The research design is a case study applied in Ain Shams University as a model of public universities in a developing country. This research makes a new research contribution by suggesting a futuristic proposal for improving the quality of the strategic decision-making process in universities through employing expert systems that are based on the theoretical framework of the research and the results of the field study.

Shared decision-making and the lessons learned about decision regret in cancer patients

The commentary presents reflections on the literature on post-treatment cancer patient regret. Even though a lot of effort has been made to increase patient satisfaction by engaging them in medical decisions, patient regret remains present in clinical settings. In our commentary, we identify three main aspects of shared decision-making that previously have been shown to predict patient regret. Based on these findings, we provide recommendations for physicians involved in the shared decision-making process. In addition, we make methodological suggestions for future research in the field.

Data as a Lens for Understanding what Constitutes Credibility in Asylum Decision-making

In asylum decision-making, legal authorities rely on the criterion "credibility" as a measure for determining whether an individual has a legitimate asylum claim; that is, whether they have a well-founded fear of persecution upon returning to their country of origin. Nation states, international institutions, and NGOs increasingly seek to leverage data-driven technologies to support such decisions, deploying processes of data cleaning, contestation, and interpretation. We qualitatively analyzed 50 asylum cases to understand how the asylum decision-making process in Denmark leverages data to configure individuals as credible (or not). In this context, data can vary from the applicant's testimony to data acquired on the applicant from registers and alphanumerical data. Our findings suggest that legal authorities assess credibility through a largely discretionary practice, establishing certainty by ruling out divergence or contradiction between the different forms of data and documentation involved in an asylum case. As with other reclassification processes [following Bowker and Star 1999], credibility is an ambiguous prototypical concept for decision-makers to attempt certainty, especially important to consider in the design of data-driven technologies where stakeholders have differential power.

Deconstructing bias: The decision-making process among child protective service workers in Chile

Worldwide, there has been a push toward reforming or abolishing child welfare systems because of systemic bias against families. Few studies have examined the role of bias in decision-making processes among child welfare practitioners, especially in child welfare systems in processes of change/reform. This qualitative study utilized discussion groups with child welfare teams to examine how professionals navigated the decision-making process in cases of child maltreatment. A grounded theory analysis revealed that professionals deconstruct macro, mezzo, and micro biases as they make decisions. Implications for global social work, such as self-reflection and structural changes, and for future research are explored.

Simulation study of crowd dynamics in pedestrian evacuation concerning panic contagion: A cellular automaton approach

The study of the panic evacuation process is of great significance to emergency management. Panic not only causes negative emotions such as irritability and anxiety, but also affects the pedestrians decision-making process, thereby inducing the abnormal crowd behavior. Prompted by the epidemiological SIR model, an extended floor field cellular automaton model was proposed to investigate the pedestrian dynamics under the threat of hazard resulting from the panic contagion. In the model, the conception of panic transmission status (PTS) was put forward to describe pedestrians' behavior who could transmit panic emotions to others. The model also indicated the pedestrian movement was governed by the static and hazard threat floor field. Then rules that panic could influence decision-making process were set up based on the floor field theory. The simulation results show that the stronger the pedestrian panic, the more sensitive pedestrians are to hazards, and the less able to rationally find safe exits. However, when the crowd density is high, the panic contagion has a less impact on the evacuation process of pedestrians. It is also found that when the hazard position is closer to the exit, the panic will propagate for a longer time and have a greater impact on the evacuation. The results also suggest that as the extent of pedestrian's familiarity with the environment increases, pedestrians spend less time to escape from the room and are less sensitive to the hazard. In addition, it is essential to point out that, compared with the impact of panic contagion, the pedestrian's familiarity with environment has a more significant influence on the evacuation.