The use of intellectual capital information by sell-side analysts in company valuation

This paper investigates the role of intellectual capital information (ICI) in sell-side analysts’ fundamental analysis and valuation of companies. Using in-depth semi-structured interviews, it penetrates the black box of analysts’ valuation decision-making by identifying and conceptualising the mechanisms and rationales by which ICI is integrated within their valuation decision processes. We find that capital market participants are not ambivalent to ICI, and ICI is used: (1) to form analysts’ perceptions of the overall quality, strengths and future prospects of companies; (2) in deriving valuation model inputs; (3) in setting price targets and making investment recommendations; and (4) as an important and integral element in analyst–client communications. We show that: there is a ‘pecking order’ of mechanisms for incorporating ICI in valuations, based on quantifiability; IC valuation is grounded in valuation theory; there are designated entry points in the valuation process for ICI; and a number of factors affect analysts’ ICI use in valuation. We also identify a need to redefine ‘value-relevant’ ICI to include non-price-sensitive information; acknowledge the boundedness and contextuality of analysts’ rationality and motives of their ICI use; and the important role of analyst–client meetings for ICI communication

Information and participation in decision-making about treatment: a qualitative study of the perceptions and preferences of patients with rheumatoid arthritis.

Objectives: To elicit the perceptions and preferences of patients with rheumatoid arthritis regarding information and participation in treatment decision-making. To analyse the patients’ narratives on the background of the ethical discourse on various approaches to treatment decisionmaking. Design: In-depth interviews with themes identified using principles of grounded theory. Participants: 22 patients with long-standing rheumatoid arthritis. Main outcome measures: Qualitative data on patients’ perceptions and preferences regarding information and participation in decision-making about treatment. Results: Decision-making about treatment has been described by the patients as a process consisting of different stages with shifting loci of control and responsibility. Patients initially received one treatment recommendation and were not aware of alternative treatment options. Those participants in this study who wanted information about negative effects of a treatment cited ‘‘interest in one’s own health’’ and the potential ‘‘use of information’’ as reasons for their preference. The physicians’ expert knowledge and clinical experience regarding the effects of medication were cited as arguments by patients for a treatment recommendation. Conclusions: The patients’ accounts of decision-making about treatment differ from models of physician–patient relationship that have been put forward in ethical discourse. These differences may be relevant with respect to the starting point of an ethical analysis of treatment decision-making. Patients’ accounts with respect to a lack of information on treatment alternatives point to ethically relevant challenges regarding treatment decision-making in clinical practice

Advancing Alternative Analysis: Integration of Decision Science.

Decision analysis-a systematic approach to solving complex problems-offers tools and frameworks to support decision making that are increasingly being applied to environmental challenges. Alternatives analysis is a method used in regulation and product design to identify, compare, and evaluate the safety and viability of potential substitutes for hazardous chemicals.Assess whether decision science may assist the alternatives analysis decision maker in comparing alternatives across a range of metrics.A workshop was convened that included representatives from government, academia, business, and civil society and included experts in toxicology, decision science, alternatives assessment, engineering, and law and policy. Participants were divided into two groups and prompted with targeted questions. Throughout the workshop, the groups periodically came together in plenary sessions to reflect on other groups' findings.We conclude the further incorporation of decision science into alternatives analysis would advance the ability of companies and regulators to select alternatives to harmful ingredients, and would also advance the science of decision analysis.We advance four recommendations: (1) engaging the systematic development and evaluation of decision approaches and tools; (2) using case studies to advance the integration of decision analysis into alternatives analysis; (3) supporting transdisciplinary research; and (4) supporting education and outreach efforts

Infusing a Person Centered Approach Into Transition Planning For Students with Developmental Disabilities

This is the first of two white papers reflective of the work of nine separate demonstration sites interested in integrating person-centered practices into the design and delivery of transition services for high school youth who have developmental disabilities. The reader is provided with an overview of the project and a description of the four universal criteria that each site agreed to adhere to as they designed program approaches that were uniquely tailored to their specific demographics. After a look at the transition policy current to 2001, the paper reveals early project findings regarding the strengths and gaps for person-centered transition planning as culled from project reports. A discussion of where person-centered planning “fits” within the transition process is placed in the context of three primary core components that should be reflected in all transition service programs and a model for infusing person-centered planning is offered. Finally, recommendations for implementing or furthering these practices are introduced along with the contact information for each of the participating demonstration sites

Context-Aware Systems for Sequential Item Recommendation

Quizlet is the most popular online learning tool in the United States, and is used by over 2/3 of high school students, and 1/2 of college students. With more than 95% of Quizlet users reporting improved grades as a result, the platform has become the de-facto tool used in millions of classrooms. In this paper, we explore the task of recommending suitable content for a student to study, given their prior interests, as well as what their peers are studying. We propose a novel approach, i.e. Neural Educational Recommendation Engine (NERE), to recommend educational content by leveraging student behaviors rather than ratings. We have found that this approach better captures social factors that are more aligned with learning. NERE is based on a recurrent neural network that includes collaborative and content-based approaches for recommendation, and takes into account any particular student's speed, mastery, and experience to recommend the appropriate task. We train NERE by jointly learning the user embeddings and content embeddings, and attempt to predict the content embedding for the final timestamp. We also develop a confidence estimator for our neural network, which is a crucial requirement for productionizing this model. We apply NERE to Quizlet's proprietary dataset, and present our results. We achieved an R^2 score of 0.81 in the content embedding space, and a recall score of 54% on our 100 nearest neighbors. This vastly exceeds the recall@100 score of 12% that a standard matrix-factorization approach provides. We conclude with a discussion on how NERE will be deployed, and position our work as one of the first educational recommender systems for the K-12 space

Development and preliminary evaluation of a clinical guidance programme for the decision about prophylactic oophorectomy in women undergoing a hysterectomy

Objectives: To develop a decision analysis based and computerised clinical guidance programme (CGP) that provides patient specific guidance on the decision whether or not to undergo a prophylactic oophorectomy to reduce the risk of subsequent ovarian cancer and to undertake a preliminary pilot and evaluation. Subjects: Women who had already agreed to have a hysterectomy who otherwise had no ovarian pathology. Setting: Oophorectomy decision consultation at the outpatient or pre-admission clinic. Methods: A CGP was developed with advice from gynaecologists and patient groups, incorporating a set of Markov models within a decision analytical framework to evaluate the benefits of undergoing a prophylactic oophorectomy or not on the basis of quality adjusted life expectancy, life expectancy, and for varying durations of hormone replacement therapy. Sensitivity analysis and preliminary testing of the CGP were undertaken to compare its overall performance with established guidelines and practice. A small convenience sample of women invited to use the CGP were interviewed, the interviews were taped and transcribed, and a thematic analysis was undertaken. Results: The run time of the programme was 20 minutes, depending on the use of opt outs to default values. The CGP functioned well in preliminary testing. Women were able to use the programme and expressed overall satisfaction with it. Some had reservations about the computerised format and some were surprised at the specificity of the guidance given. Conclusions: A CGP can be developed for a complex healthcare decision. It can give evidence-based health guidance which can be adjusted to account for individual risk factors and reflects a patient’s own values and preferences concerning health outcomes. Future decision aids and support systems need to be developed and evaluated in a way which takes account of the variation in patients’ preferences for inclusion in the decision making process

EXPLORING ALGORITHMIC EXPERIENCES IN OTT: WITH A MIXED-METHODS APPROACH

This paper addresses the challenge of 'poverty in the midst of abundance' in OTT services, where a vast array of content makes it difficult for users to find what suits their tastes, often leading to subscription cancellations. U.S. market studies show users spend an average of 10.5 minutes searching for content, while in South Korea, they experience psychological fatigue during this process. This indicates a need for improved recommendation algorithms to enhance user experience and reduce service churn The research focuses on identifying attributes in OTT recommendation algorithms that users prefer, aiming to understand which specific features of recommendations are most valued by users. Findings reveal that effective recommendation systems, tailored to user preferences and feedback, can significantly enhance the user experience. Improved search interfaces and content curation are crucial for increasing user trust and satisfaction. The paper provides an academic foundation for understanding algorithmic interplay in OTT services and practical guidance for companies to develop more effective recommendation strategies. This research underscores the importance of user-centric approaches in OTT platforms to address the content overload problem and enhance overall service qualit

The influence of national culture on the attitude towards mobile recommender systems

This is the post-print version of the final paper published in Technological Forecasting and Social Change. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.This study aimed to identify factors that influence user attitudes towards mobile recommender systems and to examine how these factors interact with cultural values to affect attitudes towards this technology. Based on the theory of reasoned action, belief factors for mobile recommender systems are identified in three dimensions: functional, contextual, and social. Hypotheses explaining different impacts of cultural values on the factors affecting attitudes were also proposed. The research model was tested based on data collected in China, South Korea, and the United Kingdom. Findings indicate that functional and social factors have significant impacts on user attitudes towards mobile recommender systems. The relationships between belief factors and attitudes are moderated by two cultural values: collectivism and uncertainty avoidance. The theoretical and practical implications of applying theory of reasoned action and innovation diffusion theory to explain the adoption of new technologies in societies with different cultures are also discussed.National Research Foundation of Korea Grant funded by the Korean governmen