Predicting satisfaction: Perceived decision quality by decision-makers in Web-based group decision support systems

Individual Momentary Experiences of Neighborhood Public Spaces: Results of a Virtual Environment Based Stated Preference Experiment

Although it has become increasingly recognized that the spatial design of public space plays an important role in the perceived quality of the built environment by users, there is still little known about the influences of specific attributes on individuals’ experiences. Therefore, this study aims at (1) identifying the factors underlying momentary experiences in public space given both cognitive (satisfaction) and affective (emotion) responses and (2) understanding the experiences as a function of green and design attributes and analyzing the heterogeneity in preferences. To achieve the research goals, this study innovatively introduced an online video-based stated preference experiment. A national sample of 316 individuals from the Netherlands were invited to complete the experiment. The results of a factor analysis indicate that the momentary experiences of the environments presented can be reduced to a single preference dimension capturing the cognitive as well as affective elements of experiences. The results of a latent class regression analysis furthermore show that there is substantial heterogeneity on how attributes are experienced. Three classes emerge which differ in terms of satisfaction with life and satisfaction with availability of green in the current living environment on dwelling, neighborhood, and city level. Presence of trees, vertical green on façades, and grass surfaces were found to be most important for individuals’ experiences generally. The paper provides a new understanding of how spatial attributes in neighborhood public spaces can influence individuals’ momentary experiences. For cities pursuing both greenification and densification, this study offers quantitative evidence to support the selection of design attributes of neighborhood public spaces which can evoke positive experiences.

Anomaly Detection on Natural Language Processing to Improve Predictions on Tourist Preferences

Argumentation-based dialogue models have shown to be appropriate for decision contexts in which it is intended to overcome the lack of interaction between decision-makers, either because they are dispersed, they are too many, or they are simply not even known. However, to support decision processes with argumentation-based dialogue models, it is necessary to have knowledge of certain aspects that are specific to each decision-maker, such as preferences, interests, and limitations, among others. Failure to obtain this knowledge could ruin the model’s success. In this work, we sought to facilitate the information acquisition process by studying strategies to automatically predict the tourists’ preferences (ratings) in relation to points of interest based on their reviews. We explored different Machine Learning methods to predict users’ ratings. We used Natural Language Processing strategies to predict whether a review is positive or negative and the rating assigned by users on a scale of 1 to 5. We then applied supervised methods such as Logistic Regression, Random Forest, Decision Trees, K-Nearest Neighbors, and Recurrent Neural Networks to determine whether a tourist likes/dislikes a given point of interest. We also used a distinctive approach in this field through unsupervised techniques for anomaly detection problems. The goal was to improve the supervised model in identifying only those tourists who truly like or dislike a particular point of interest, in which the main objective is not to identify everyone, but fundamentally not to fail those who are identified in those conditions. The experiments carried out showed that the developed models could predict with high accuracy whether a review is positive or negative but have some difficulty in accurately predicting the rating assigned by users. Unsupervised method Local Outlier Factor improved the results, reducing Logistic Regression false positives with an associated cost of increasing false negatives.

Emergency water supply decision-making of transboundary river basin considering government–public perceived satisfaction

The emergency supply of transboundary water resources is a prominent problem affecting the social and economic development of basin countries. However, current water supply decisions on transboundary water resources may ignore the psychological perception of multi-stakeholders, and the evolution of emergencies increases the uncertainty of decision making. Both factors would lead to the low acceptance of water-related decisions. Utility satisfaction, perceived losses, and quantity satisfaction were selected in this paper to identify the perceived satisfaction of upstream governments, downstream governments, and the public, respectively, over multiple decision-making stages. A modeling framework combining prospect theory and the multi-stage multi-objective programming methodology was then developed to measure the perceived satisfaction of different stakeholders in a watershed under emergency. A two-stage NSGA-II and TOPSIS based approach was adopted to find the optimal compromise solution to solve the model. The framework was applied in the Lancang–Mekong River basin to provide suggestions to decision makers. Upstream decision makers must choose a moderate proportional fairness degree when making emergency decisions to maximize the perceived satisfaction of all stakeholders. Meanwhile, the perceived loss of downstream countries with low water demand should be considered first in the formulation of emergency water supply plans. Furthermore, although water supply from upstream countries can improve perceived water quantity satisfaction of downstream publics, additional actions must still be taken to change the traditional concepts of the public.

Achieving structured knowledge management with a novel online group decision support system

Knowledge management (KM) is a complicated process that involves socialization, externalization, combination, and internalization and requires close collaboration among the people involved. Although Nonaka proposed the SECI (Socialization, Externalization, Combination, Internalization) model and the concept of Ba, which provides a process-oriented view of knowledge creation and transfer, practicing it is rather ad hoc. COVID-19 has provided a chance for practitioners to find a new method for KM. In this study, we adapted a group problem-solving system called TeamSpirit and structured it as a Ba for the SECI model. We then compared TeamSpirit with two other implementations of Ba, email and face-to-face communication, to evaluate their effects on knowledge externalization, knowledge combination, and knowledge internalization. Then, we evaluated whether these knowledge-conversion processes could improve knowledge acquisition and intention to share knowledge. A 3 × 2 mixed factorial design experiment was conducted. The results show that (a) TeamSpirit was better than the others, and face-to-face was better than email for each of the three knowledge conversion processes (externalization, combination, and internalization) and (b) the better the team’s knowledge conversion process lead, the stronger its knowledge acquisition and knowledge-sharing intention.

The Loyalty of Tourism in Synagogues: The Special Case of the Synagogue of Córdoba

Tourism in synagogues is an activity which is increasing in popularity due to the current interest in cultural and patrimonial heritage. The synagogue in Córdoba has become an irreplaceable Jewish tourist destination in Spain due to its origins, construction, conservation, and recognition by international organizations. This article analyzes the loyalty of tourism in synagogues using variables such as satisfaction, loyalty and the expected and perceived value of the destination and the historical monument using a structural research equations methodology. No previous studies on tourism at synagogues using structural equations have been made. The data was obtained by means of a survey given to 350 tourists visiting this cultural heritage site. The results showed that there is strong loyalty to the heritage site as well as the destination.