Real Decision Research Articles

Reporting bias and feedback effect

AbstractStock prices often provide firm managers with new information that can be used in real decisions. Studies generally focus on the ex ante disclosure policy and show that the presence of market feedback crowds out firms' disclosure. We instead examine a manager's ex post biasing incentives and find that market feedback amplifies overreporting bias, but not necessarily underreporting bias, due to three interacting effects. First, the manager biases the report more with feedback since decreased information quality crowds in the speculator's private information acquisition and improves investment efficiency, regardless of the reporting scenario (the information rationing effect). Second, the manager biases more in the overreporting scenario and less in the underreporting scenario, because reporting more favorable information crowds in private information acquisition, as the speculator expects a higher subsequent investment and therefore higher trading profits (the investment scale effect). Third, market feedback influences reporting bias not only through the speculator's information acquisition but also directly through the market maker's pricing function. Specifically, the market maker decreases the price discount, as he expects that the manager may learn correct information from the price and may invest more efficiently. Expecting a lower price penalty in the presence of feedback, the manager biases more in the overreporting scenario and less in the underreporting scenario (the investment correction effect). Overall, our results suggest that granting firms reporting discretion could improve investment efficiency and firm value when managers can learn through price.

Being Well-in with the Joneses? A Lab-in-the-Field Experiment on Conspicuous Consumption among Rural Communities

Conspicuous consumption, specialised consumption of high visibility but without apparent economic benefit, is reducing investment in productive assets and thus hindering economic development in low-income countries. In previous research, the phenomenon was commonly explained by status-seeking and herding behaviour. Our study follows a novel angle in testing the role of risk sharing, assuming that investment into social status is perceived to increase access to informal credits in times of crises. We conduct a random-effects hurdle model along a lab-in-the-field experiment along a sample of 197 wheat farmers in Uzbekistan, a country characterised by high levels of risk and uncertainty. Within our experimental setup, both risk attitude and real-life risk management decision are found to be significant determinants of conspicuous consumption. Our findings support the notion of a complex decision-making process with risk sharing as one important motivator. Providing first empirical evidence on the topic, our findings have implications beyond our narrowly defined study case: We argue that strengthening options of formal risk-sharing tools might remove one of the motivators for conspicuous consumption; thus, it could improve the economic welfare of low-income households worldwide by allowing for more productive investment of scarce financial resources.

What are tenants demanding the most? A machine learning approach for the prediction of time on market

PurposeThis is the first article to apply a machine learning approach to the analysis of time on market on real estate markets.Design/methodology/approachThe random survival forest approach is introduced to the real estate market. The most important predictors of time on market are revealed and it is analyzed how the survival probability of residential rental apartments responds to these major characteristics.FindingsResults show that price, living area, construction year, year of listing and the distances to the next hairdresser, bakery and city center have the greatest impact on the marketing time of residential apartments. The time on market for an apartment in Munich is lowest at a price of 750 € per month, an area of 60 m2, built in 1985 and is in a range of 200–400 meters from the important amenities.Practical implicationsThe findings might be interesting for private and institutional investors to derive real estate investment decisions and implications for portfolio management strategies and ultimately to minimize cash-flow failure.Originality/valueAlthough machine learning algorithms have been applied frequently on the real estate market for the analysis of prices, its application for examining time on market is completely novel. This is the first paper to apply a machine learning approach to survival analysis on the real estate market.

Moderating the role of trust toward intention to adopt cloud computing by academic libraries in North-Eastern Nigerian

Cloud computing has since been recognized as a robust technology that speeds and simplifies organizational service delivery. But its adoption especially in African countries and academic libraries is still at the nascent stage. This study aims to find the contributing determinants of cloud computing adoption in North-Eastern Nigerian academic libraries using Technological and Organizational variables of Technology-Organization-Environment theory(TOE) and Diffusion of Innovation Theory (DOI). A quantitative approach using a survey research design was employed. Seventy-eight academic libraries in North-Eastern Nigeria formed the population of the study and census enumeration is used which indicates that all the academic libraries are considered. Results revealed that five out of six determinants are significant for the adoption of cloud computing in academic libraries. While moderation effect results revealed that only one determinant is moderated by the adopted moderator. This implies that the significant determinants should be given more consideration when deciding to adopt cloud computing. Equally, the study’s results will guide academic libraries’ decision-makers that include the Chief Librarian, Deputy Librarian and Head of ICT unit who are responsible for deciding on the rejection or accepting innovative technology in the library to make fruitful and realistic decisions when deciding to adopt cloud computing. Theoretically, the results will contribute to the body of knowledge especially in the librarianship field. Finally, the study recommends among others that special attention should be given to those significant determinants when deciding to adopt cloud computing in academic libraries. Future studies should employ more variables and use another factor as a moderator. Equally future studies should cover more areas beyond North-Eastern Nigeria.

Abstract B042: Analysis of predictive indicators of invasive breast cancer: Modeling survival rates

Abstract Objective: We aim to investigate indicators of risk for invasive breast cancer (IBC) and to predict and assess the impact of these risk indicators on survival rates in breast cancer (BC) patients who received various therapies. Background: It has been identified that several indicators including BC subtypes, age at diagnosis, history of breast disease, menopausal status, estrogen receptor (ER) status, progesterone receptor (PR) status, human epidermal growth receptor (HER2) status, tumor stage, tumor size, among others, can cause genomic alterations and driver mutations in BC, leading to invasive carcinogenesis. To assure improved BC outcomes, we investigate the influence of BC indicators on survival rates in patients receiving hormone therapy, chemotherapy and radiotherapy. Methods: This study uses the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, which included 31 clinical indicators, m-RNA levels z-score for 331 genes, and mutation in 175 genes for 1904 BC patients. Both bivariate and multivariate associations were examined using logistic regression and Cox proportional hazard models. The multivariate logistic regression analysis is used to determine predictors of IBC. The Cox proportional hazard model was also used to evaluate the magnitude of the impacts of clinical and demographic variables on IBC, as well as the effects of therapy on survival. All analyses for this investigation were carried out using SAS. A p-value < 0.05 was considered statistically significant. Results: We examined 1,043 patients (with the median age 61±13.1 years) from a total of 1904 for this study: 78.8% of patients had breast invasive ductal carcinoma; 87.4% were HER2 negative; 22.8%, 60.7%, and 67.6% received chemotherapy, hormone therapy, and radiotherapy treatments, respectively; 56.9% died but only 34.6% died from the disease; the average overall survival months was 127.3±77.6 months; and the most common age group was "65+ years" (39.4%). Chemotherapy (p=0.0474), neoplasm histologic grade (p=0.0015), Nottingham prognostic index (p<.0001), radiation (p=0.0068), and BReast CAncer gene 1 (BRCA1) (p=0.0251) were all associated with IBC. Overall survival was also correlated to age at diagnosis (p=0.0002), neoplasm histologic grade (p<.0001), lymph nodes (p<.0001), mutation count (p<.0001), Nottingham prognostic index (p<.0001), radiotherapy (p=0005), tumor size (p<.0001), tumor stage (p<.0001), BRCA1 (p=0.250), PTEN (p<.0001), and TP53 (p=0.0248). The overall accuracy of the model for each IBC and survival status was 82.1% and 74.2%, respectively. Conclusion: Our analysis demonstrated the importance of predictive analysis using the logistic regression model and survival analysis using the Cox proportional hazard model in cancer research. Predictive and survival analysis results aided in predicting the invasiveness and survival rate of BC among the study participants. In general, predictive and survival analyses are more accurate and may be used to make more realistic treatment decisions and precision oncology. Citation Format: Olumide Arigbede, Gebre-Egziabhe Kiros. Analysis of predictive indicators of invasive breast cancer: Modeling survival rates [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr B042.

The price of clean air – quantifying air pollution exposure in real estate decisions

PurposeThe purpose of this paper is to explore the significance impact of air quality as a contributing factor on residential property rents by applying geo-informatics to economic issues. Since air pollution poses a severe health threat, city residents should have a right to know about the (invisible) hazards they are exposed to.Design/methodology/approachWithin spatial-temporal modeling of air pollutants in Berlin, Germany, three interpolation techniques are tested. The most suitable one is selected to create seasonal maps for 2018 and 2021 with pollution concentrations for particulate matter values and nitrogen dioxide for each 1,000 m2 cell within the administrative boundaries. Based on the evaluated pollution particulate matter values, which are used as additional variables for semi-parametric regressions the impact of the air quality on rents is estimated.FindingsThe findings reveal a compelling association between air quality and the economic aspect of the residential real estate market, with noteworthy implications for both tenants and property investors. The relationship between air pollution variables and rents is statistically significant. However, there is only a “willingness-to- pay” for low particulate matter values, but not for nitrogen dioxide concentrations. With good air quality, residents in Berlin are willing to pay a higher rent (3%).Practical implicationsThese results suggest that a “marginal willingness-to-pay” occurs in a German city. The research underscores the multifaceted impact of air quality on the residential rental market in Berlin. The evidence supports the notion that a cleaner environment not only benefits human health and the planet but also contributes significantly to the economic bottom line of property investors.Originality/valueThe paper has a unique data engineering approach. It collects spatiotemporal data from network of state-certified measuring sites to create an index of air pollution. This spatial information is merged with residential listings. Afterward non-linear regression models are estimated.

AI in Court: Facing Today's Legal Challenges

AI is changing how courtrooms work. This brings both advantages and challenges for the legal system. As AI gets better, courts use it more in different parts of legal cases. From predicting outcomes to virtual helpers, AI is changing how cases are handled and decided. But using AI in law also raises big ethical and legal questions. As the legal world deals with these issues, it's clear AI isn't just for the future – it's here now, and it affects real court decisions.

Techniques for Enhancing Security in Industrial Control Systems

Increasingly Industrial Control Systems (ICS) systems are being connected to the Internet to minimise the operational costs and provide additional flexibility. These control systems such as the ones used in power grids, manufacturing and utilities operate continually and have long lifespans measured in decades rather than years as in the case of Information Technology (IT) systems. Such industrial control systems require uninterrupted and safe operation. However, they can be vulnerable to a variety of attacks, as successful attacks on critical control infrastructures could have devastating consequences to the safety of human lives as well as a nation’s security and prosperity. Furthermore, there can be a range of attacks that can target ICS and it is not easy to secure these systems against all known attacks let alone unknown ones. In this paper, we propose a software enabled security architecture using Software Defined Networking (SDN) and Network Function Virtualisation (NFV) that can enhance the capability to secure industrial control systems. We have designed such an SDN/NFV enabled security architecture and developed a Control System Security Application (CSSA) in SDN Controller for enhancing security in ICS by achieving real time situational awareness and dynamic policy-driven decision making across the network infrastructure. In particular, CSSA can be used for establishing secure path for end-to-end communication between devices and also deal against certain specific attacks namely denial of service attacks, from unpatched vulnerable control system components and securing the communication flows from the legacy devices that do not support any security functionality. We also discuss how CSSA provides reliable paths for safety critical messages in control systems. We discuss the prototype implementation of the proposed architecture and the results obtained from our analysis.

Automated, real-time material detection during ultrashort pulsed laser machining using laser-induced breakdown spectroscopy, for process tuning, end-pointing, and segmentation.

The rapid, high-resolution material processing offered by ultrashort pulsed lasers enables a wide range of micro and nanomachining applications in a variety of disciplines. Complex laser processing jobs conducted on composite samples, require an awareness of the material type that is interacting with laser both for adjustment of the lasering process and for endpointing. This calls for real-time detection of the materials. Several methods such as X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and energy dispersive X-Ray spectroscopy (EDS) can be used for material characterization. However, these methods often need interruption of the machining process to transfer the sample to another instrument for inspection. Such interruption significantly increases the required time and effort for the machining task, acting as a prohibitive factor for many laser machining applications. Laser induced breakdown spectroscopy (LIBS) is a powerful technique that can be used for material characterization, by analyzing a signal that is generated upon the interaction of laser with matter, and thus, it can be considered as a strong candidate for developing an in-situ characterization method. In this work, we propose a method that uses LIBS in a feedback loop system for real time detection and decision making for adjustment of the lasering process on-the-fly. Further, use of LIBS for automated material segmentation, in the 3D image resulting from consecutive lasering and imaging steps, is showcased.

Analisis Struktur Kepribadian Tokoh Afa Dalam Novel El Verano Karya Pia Devina

Abstract: The aim of this research is to apply analysis of Sigmund Freud's theory, namely personality structure, namely Ideas, Ego, Super Ego to literary works. The work is based on the novel genre with the title El Verano by Pia Devina. This research uses a literary psychology approach with reading and analyzing analysis techniques. This research method is qualitative, based on Sigmund Freud's psychoanalytic theory. The results obtained show that the idea of ​​the character Afa has a strong id, seen from the many id impulses that appear in him, such as impulses to act aggressively, avoid responsibility, attack others, express emotions directly, and avoid fear. The ego of Afa's character is quite good, seen from his ability to control his id impulses, express his emotions naturally, make realistic decisions, understand other people's feelings, build relationships with other people, and achieve satisfaction. Super Ego The character Afa has a developed superego, which can be seen from his ability to feel various kinds of moral emotions, such as feelings of guilt, regret, shame, wanting to be a better person, wanting to forgive, wanting to be a strong person, and feelings of satisfaction.

Social Media, Endometriosis, and Evidence-Based Information: An Analysis of Instagram Content.

Social media platforms are used for support and as resources by people from the endometriosis community who are seeking advice about diagnosis, education, and disease management. However, little is known about the scientific accuracy of information circulated on Instagram about the disease. To fill this gap, this study analysed the evidence-based nature of content on Instagram about endometriosis. A total of 515 Instagram posts published between February 2022 and April 2022 were gathered and analysed using a content analysis method, resulting in sixteen main content categories, including "educational", which comprised eleven subcategories. Claims within educational posts were further analysed for their evidence-based accuracy, guided by a process which included fact-checking all claims against the current scientific evidence and research. Of the eleven educational subcategories, only four categories (cure, scientific article, symptoms, and fertility) comprised claims that were at least 50% or greater evidence-based. More commonly, claims comprised varying degrees of evidence-based, mixed, and non-evidence-based information, and some categories, such as surgery, were dominated by non-evidence-based information about the disease. This is concerning as social media can impact real-life decision-making and management for individuals with endometriosis. Therefore, this study suggests that health communicators, clinicians, scientists, educators, and community groups trying to engage with the endometriosis online community need to be aware of social media discourses about endometriosis, while also ensuring that accurate and translatable information is provided.

Virtual Storefronts and Real Decisions: A Study of Consumer Behavior in E-commerce

Virtual Storefronts and Real Decisions: A Study of Consumer Behavior in E-commerce

Triskaidekaphobia: The Role of the Dreaded 13th Floor in Shaping Investment Preferences in the Indian Residential Real Estate Market

This study investigates the influence of triskaidekaphobia, the fear of the number 13, on investment preferences in the Indian residential real estate market, specifically examining attitudes towards properties on the 13th floor in high-rise buildings. Originating in Western cultures, triskaidekaphobia has transcended cultural boundaries, influencing the Indian market, which is traditionally shaped by a blend of cultural, religious, and socio-economic factors, including beliefs like Vastu Shastra. Using a quantitative research methodology, the study surveyed 500 participants from various demographic backgrounds across India. The findings reveal a significant awareness of triskaidekaphobia among the respondents, with a majority strongly believing in this superstition. This belief translates into a considerable reluctance to invest in properties on the 13th floor, with many respondents and potential developers expressing a preference to exclude or rename this floor. Statistical analyses, including correlation and regression, indicate a negative relationship between belief in triskaidekaphobia and willingness to invest in the 13th floor. These findings suggest that triskaidekaphobia significantly influences real estate investment decisions in India, reflecting the deep-rooted impact of cultural beliefs on economic behaviors. The study contributes to the understanding of cultural influences in the real estate market and offers insights for real estate developers, urban planners, and policymakers. It highlights the importance of cultural sensitivity in business strategies and the need to consider local beliefs and superstitions in market analyses. The research suggests that addressing the irrational basis of triskaidekaphobia through investor education could open new market opportunities and promote more rational investment choices.

A DE Novo multi criteria heterogeneous group decision making approach for green performance assessment of CNC machine tools

In the contemporaneous sustainable manufacturing scenario and fourth industrial revolutions, requirements of most cutting-edge CNC machine tools are indispensable for finished products with high accuracy, precision and green complaints in particular. Such requirements have impelled the advanced manufacturing industries to evaluate and choose the proper CNC machine tools for best customized performances. In the face of proper and effective green evaluation, this paper incorporates a heterogeneous expert group based decision framework considering multiple significant technical and green criteria by assessing relative importance of diverse conflicting criteria having substantial contribution in performance analysis of CNC machine tools. As a demonstration of the suggested mathematical model, three real life decision making problems related to 3 axes-CNC machine tools based on the collected quantitative and linguistic data from catalogues, manufacturer’s portals, questionnaires, customer reviews etc. are established. The calculated findings are close to those obtained by previous researchers as well as are verified by well-established techniques. Besides, sensitivity and statistical analysis are performed to examine the robustness and stability of the ranking orders of the alternatives as well as to investigate the efficacy and consistency of the proposed method. Hence thus proposed formulated MCDM approach proves to be a highly effective and reliable decision making tool for choosing the most suitable CNC machine tools.

Information processes in stochastic models for strategic thinking operations

It constitutes a particularly valuable adoption that the family of infinitely divisible distributions is frequently employed as a very suitable choice for investigating theoretical and practical properties of structural probabilistic concepts. In particular, this family is valuable in construction and study of mathematical expressions, or equivalently stochastic models, with extensive practical applicability. The present paper focuses on the development and investigation of a stochastic model by making use of the extremely useful concept of infinitely divisibility. In addition, the paper offers an application of the introduced mathematical expressions in making realistic decisions in the extremely important area of various information processes.

Decision-making models applied to IT Project selection considering intangible assets

How to identify the best IT project to invest in? Investing means spending resources with the intention of obtaining future benefits. Can this evaluation be done in a sustained way that minimizes the failure of a wrong choice and, therefore, some way to guarantee those benefits? This study intends to help answer these questions by building a decision-making tool, based on the existing multipurpose and adapting it to the reality of the IT Project Portfolio. The tool design is based on an in-depth analysis of IT projects, valuing their tangible and intangible characteristics to facilitate the organization’s management or decision-makers. Following an inductive thinking, the real decision making matrices shall be dissected to establish a future generalization in the form of a new tool to be applied to a specific scenario. A mixed strategy will be adopted (quantitative strategy to establish cause-effect relationships and qualitative strategy to understand the intangible factors associated with IT projects).

VALIDATING THE MEASURES AND ASSESSING THE DIMENSIONS OF PURCHASE BEHAVIOR IN APARTMENT SCENARIO IN INDIAN PERSPECTIVE

As the data and technology-based interference in real estate decision making is increasing, the customer work behavior and builder technology usage is seemingly shaping value propositions in more than one way. While recent research has begun to take technology imprint as seriously yet very few studies have sought to validate the measures and assess the dimensions of purchase behavior in apartment scenario in Indian perspective. This research begins to address the instrument’s construct validity by arriving at a single instrument with confirmatory factor analysis and proposes broad dimensions that fit the set of data effectively. Results have practical implications for real estate marketers but also conceptual implications pertaining to application of these benchmarks in industrial perspective.

ALGORITHM OF FUZZY PRIORITIES FOR ALTERNATIVE SOLUTIONS

It is rather problematic to approximate the real decision management process with a mathematical model. Most decisions in the economy are complex in structure, multi-criteria tasks containing a set of descriptive, probabilistic, undefined characteristics. Such problems are solved by methods of system analysis. One of these methods is the method of analyzing hierarchies, which is used for models of personnel management, resource allocation, and evaluation of investment projects. The possibility of practical application of the method of analysis of hierarchies is limited by the requirement of complete certainty of alternative evaluations. Practical situations are characterized by uncertainty and limited information about the object or the decision-making process. Combining a systems approach with elements of fuzzy logic can correct this situation for the better. System models developed on the basis of the theory of fuzzy sets use the method of paired comparisons to rank management objects by factors and determine the importance of factors within the model. The article examines the algorithm for the synthesis of the vector of weighting coefficients of alternative decisions based on the matrix of pairwise comparisons with fuzzy estimates of alternatives. The possibility of combining elements of the theory of fuzzy sets and the method of analysis of hierarchies for the problem of decision-making is considered.

The Use of Gamification in the Self-Management of Patients With Chronic Diseases: Scoping Review.

Chronic disease self-management is a public health issue of worldwide concern, and gamification is an emerging strategy to improve patients' participation in chronic disease self-management. Some studies have summarized designs for the gamification of chronic disease self-management from the perspective of eHealth technology, but they have not mentioned differences in design methods, functions, and evaluation methods of gamified designs for self-management in different chronic diseases. This scoping review aims to synthesize the characteristics of realization forms, functions, and evaluation methods in chronic disease self-management gamification to improve self-management among the chronic disease population. We applied a methodological framework for scoping reviews and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. As of January 7, 2023, we systematically searched 9 databases for relevant studies from January 2012 to December 2022. Related data were extracted based on the research questions. We calculated the frequencies, charted the quantitative data, and coded the extracted material for qualitative content analysis. We retrieved 16,221 records, of which 70 (0.43%) met the eligibility criteria. In the included research, the target populations for gamified designs for self-management of chronic diseases included patients with stroke, cancer, diabetes, chronic obstructive pulmonary disease, coronary heart disease, obesity, and hypertension. Almost all studies mentioned technical support for gamification (68/70, 97%), mainly in the form of active video games (58/70, 83%); however, less than half of the studies mentioned the theoretical basis for gamification (31/70, 44%). There were 37 concepts or theories relevant to gamification design, most of which were in the field of psychology or were cross-disciplinary (n=33, 89%). Gamification for the self-management of chronic diseases has been widely recognized, including for promoting physical exercise and rehabilitation training (48/99, 48%), increasing initiative for symptom management (18/99, 18%), providing psychological support (14/99, 14%), improving cognitive function (12/99, 12%), and improving medication adherence (7/99, 7%). A total of 39 studies mentioned the gamification effect; however, we did not find a unified evaluation standard. This scoping review focuses on gamification designs for chronic disease self-management and summarizes the realization forms and functions of gamification in self-management for different patient populations. With practice in a gamified internet-based environment, patients can not only master the knowledge and skills of self-management in fascinating scenarios but also benefit from gaming experience and make better health-related decisions in real life. It is worth noting that a comprehensive evaluation of the users as well as a personalized and targeted intervention should be developed before gamification.

The Influence of the Internet on the Formation of Virtual Communities and Expansion of Boundaries of Civil Society during the Large-Scale War with the Russian Federation

The purpose of the article is to study the social impact of the use of e-communications on the state of civil society of Ukraine in the conditions of a large-scale war against the Russian Federation. To achieve the goal, the author substantiated the political component of the introduction of e-communications, features, specificity and differences of territorial and virtual communities of GS, determined ways to counter the negative effects of the manipulative mechanisms of the aggressor country. The article uses statistical, comparative, monitoring methods, systemic and functional methodological approaches. The author proves that the development of e-communications is a prerequisite for changes in the activities of organizations, communities and politicians which is becoming more and more transparent and must meet the expectations of the increasingly active HS. Changes are taking place in state and non-state bodies, providing access to them, increasing transparency, increasing the opportunity for citizens to participate in the decision-making process, etc. Politics largely depends on the opinion of citizens, their communities and organizations. Political power is becoming more decentralized, and there are more and more centers of power. The Internet leads to its decentralization, to the formation of a model of a new type of democracy, thanks to convergent processes between representative and direct democracy. Citizens are getting more and more information in real time and decisions about politics and politicians are coming in real time. Support for Internet messaging technologies increases opportunities for citizens to directly participate in politics. Analysis of unifying and network features of a new type of e-democracy and other characteristics, with which it is connected – protest and anti-system movements, are transformed in the direction of "smart communities" or "smart crowds", because their actions are implemented simultaneously in virtual and real space.