Content-based recommender support system for counselors in a suicide prevention chat helpline: Design and evaluation study

Background: The working environment of a suicide prevention helpline requires high emotional and cognitive awareness from chat counselors. A shared opinion among counselors is that as a chat conversation becomes more difficult, it takes more effort and a longer amount of time to compose a response, which, in turn, can lead to writer's block. Objective: This study evaluates and then designs supportive technology to determine if a support system that provides inspiration can help counselors resolve writer's block when they encounter difficult situations in chats with help-seekers. Methods: A content-based recommender system with sentence embedding was used to search a chat corpus for similar chat situations. The system showed a counselor the most similar parts of former chat conversations so that the counselor would be able to use approaches previously taken by their colleagues as inspiration. In a within-subject experiment, counselors' chat replies when confronted with a difficult situation were analyzed to determine if experts could see a noticeable difference in chat replies that were obtained in 3 conditions: (1) with the help of the support system, (2) with written advice from a senior counselor, or (3) when receiving no help. In addition, the system's utility and usability were measured, and the validity of the algorithm was examined. Results: A total of 24 counselors used a prototype of the support system; the results showed that, by reading chat replies, experts were able to significantly predict if counselors had received help from the support system or from a senior counselor (P=.004). Counselors scored the information they received from a senior counselor (M=1.46, SD 1.91) as significantly more helpful than the information received from the support system or when no help was given at all (M=-0.21, SD 2.26). Finally, compared with randomly selected former chat conversations, counselors rated the ones identified by the content-based recommendation system as significantly more similar to their current chats (β=.30, P<.001). Conclusions: Support given to counselors influenced how they responded in difficult conversations. However, the higher utility scores given for the advice from senior counselors seem to indicate that specific actionable instructions are preferred. We expect that these findings will be beneficial for developing a system that can use similar chat situations to generate advice in a descriptive style, hence helping counselors through writer's block

Decision-Making in Defined Contribution Pension Plans: The Case of Israel

Research suggests that the DC saving method, which is a self-guided saving method, is a contributor to exacerbation in retirees' poverty and income inequality. Economists lobby governments to provide the public with information regarding pension savings to improve the situation, and many studies conclude that employees can improve their decisions substantially by receiving pension advice. This study analyzes Israeli data of four field studies among over 1,500 subjects and the Israeli social survey data of the Israeli Central Bureau of Statistics (ICBS). The findings of the study indicate that providing employees with information regarding pension savings does not encourage them to more pro-actively manage their pension savings nor receive advice from a professional counselor. Surprisingly, the findings suggest that employees are more willing to receive pension savings advice from a counselor on behalf of the state than from a counselor who is not necessarily on behalf of the state

The Effects on Employees from the Switch to Mandatory Contributions in the University of Arkansas Retirement Plan

After the 2016 fiscal year, the University of Arkansas Retirement Plan instituted mandatory contributions for full-time employees, presumably to boost retirement savings among those least prepared for retirement. Mandatory contributions began at 1% in fiscal-year 2017 and increased annually to 5% in fiscal-year 2022. This change may have harmed employees with tight budget constraints who wish to contribute less than the minimum contribution rate. At the same time, it may have helped those who were saving less than their optimal amount due to behavioral biases. We surveyed employees at the University of Arkansas campus to assess the effects from the change to mandatory contributions and received 171 responses. Our main findings are that most respondents are unaffected by the change to mandatory contributions; a small minority are unsatisfied with the change; average contribution rates increased for all full-time employees, especially staff; a small percentage of staff, but no faculty, may have been harmed by the change; and a larger percentage of staff and faculty may have been helped. These results, however, must be interpreted with caution because they are limited by a relatively small sample size that is not representative of the employee composition at the University of Arkansas. For more robust results, a much larger survey is needed that reaches across all campuses of the University of Arkansas System to accurately assess the effects on employees from mandatory contributions

Influencing Retirement Saving Behavior with Expert Advice and Social Comparison as Persuasive Techniques

Abstract. Numerous online communities and e-commerce sites provide users with crowd-based recommendations to influence decision making about prod-ucts. Similarly, automated recommender systems often use social advice or cu-rated knowledge provided by experts to give customers personalized product recommendations. Little, however, is known about the relative strengths of these approaches in repeated-decision scenarios. We used social comparison and an expert recommendation to examine the relative effectiveness of these methods of persuasion for users making repeated retirement saving decisions. We exposed 314 performance-incentivized experiment participants to a retire-ment saving simulator where they made 34 yearly asset allocation decisions in one of three user interface conditions. The gap between participants ’ retirement goal and actual savings was smallest in the expert advice condition and signifi-cantly better than the social comparison condition. Both conditions were signif-icantly better than the control condition. In non-control conditions, users ad-justed their behavior and achieved their saving goal more effectively