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Increasing attention to sustainability has made product recovery issues increasingly significant. Although several studies portray product recovery arrangements as networks, these constellations have not been analyzed with network models. The purpose of this paper is to investigate the organizing of product recovery networks.

Previous research highlighted the need for a holistic perspective on product recovery. Industrial network theory provides such a framework, based on three dimensions of business reality: activities, resources and actors. The research method applied is a qualitative case study approach of product recovery in the PC industry.

The most significant issues in the organizing of product recovery concern the coordination of interdependent activities and the combining of physical and organizational resources. Effective organizing is contingent on interaction and information exchange among firms. Furthermore, the sorting rules applied in the product recovery process are crucial for the performance in the activity chain from disposer to end-user.

The study deals with product recovery of PCs, and needs to be supplemented with research in other empirical contexts.

The study offers companies broader perspective on their product recovery operations by illustrating how they are related to a wider network.

The study applies a novel perspective on product recovery. The analytical framework and the qualitative approach complement mainstream approaches.

Expectations ostensibly lead to the formation of hierarchies, and hierarchies are thought to improve coordination. A simulation model is introduced to determine whether expectations directly improve coordination.

Agent-based simulations of small group behavior are used to determine what rules for expectation formation best coordinate groups. Within groups of agents that have differing but unknown task abilities, pairs take turns playing a coordination game with one another. The group receives a positive payoff when one agent chooses to take a high-importance role (leader) and the other chooses a low-importance role (follower), where the payoff is proportional to the ability of the “leader.” When both individuals vie to be leader, a costly conflict gives the group information about which agent has a higher task-ability.

The rules governing individuals’ formation of expectations about one another often lead to coordination that is suboptimal: They do not capitalize on the differential abilities of group members. The rules do, however, minimize costly conflicts between individuals. Therefore, standard rules of expectation formation are only optimal when conflicts are costly or provide poor information.

Rules that govern the formation of expectations may have served an evolutionary purpose in guiding individuals towards coordination while minimizing conflict, but these psychologically hardwired rules lead to suboptimal hierarchies.

This paper looks at how well empirically observed expectation-generating rules lead to group coordination by adding a game theoretic conception of interaction to the e-state structuralism model of hierarchy formation.

While there has been discussion of the computer manipulability of classification notations from a theoretical viewpoint, there has been little, if any on practical filing. A practical filing rubric, with explanation of machine filing concepts, is presented here. Some observations on the suitability of Library of Congress and Dewey Decimal notations for computer search follow.

The Fully Automatic Syntactically‐based Indexing of Text (FASIT) system represents the contents of a document without a full parse or semantic analysis of the text. Content‐bearing units are isolated and then grouped into quasi‐synonymous classes whose main term is used to index the document. Previous experiments with FASIT demonstrated its usefulness in an associational retrieval environment; the experiment described here explores FASIT's value as a book‐indexing system. It is difficult to avoid the conclusion that this indexing approach offers the promise of being practical and effective.

When the history of the financial crisis, stock market crash, and ensuing recession of 2007–2009 is written, the appropriate focus would be on the role that “expertise” played in almost every chapter of the story. From the expertise of the mathematicians who guided the models used by financial institutions, to the expertise of those who developed new kinds of mortgage instruments that required very low down payments, to the expertise of US policymakers who told us that new regulations to encourage more widespread homeownership would be an engine of economic growth and prosperity, the actions of those who knew better eventually littered the financial landscape with their errors. In addition to the prior list, which is hardly exhaustive, perhaps the most central set of experts in the story were those associated with the Federal Reserve System, the US central bank. The Fed rarely shies away from using its expertise to cloak its choices in a cloud of jargon and technicalities, even as its every move has significant effects across the US economy and the whole globe. The Fed's decisions to keep interest rates so low after 9/11 and to seize unprecedented powers in the wake of the recession that inevitably followed that earlier policy were both the latest examples of the history of the Fed's ever-increasing claims to expertise that have led to expanding powers and new and more damaging mistakes.

This paper presents an algorithm that can elicitate (infer) all or any combination of elimination and choice expressing reality (ELECTRE) Tri-B parameters. For example, a decision maker can maintain the values for indifference, preference and veto thresholds, and the study’s algorithm can find the criteria weights, reference profiles and the lambda cutting level. The study’s approach is inspired by a machine learning ensemble technique, the random forest, and for that, the authors named the study’s approach as ELECTRE tree algorithm.

First, the authors generate a set of ELECTRE Tri-B models, where each model solves a random sample of criteria and alternates. Each sample is made with replacement, having at least two criteria and between 10% and 25% of alternates. Each model has its parameters optimized by a genetic algorithm (GA) that can use an ordered cluster or an assignment example as a reference to the optimization. Finally, after the optimization phase, two procedures can be performed; the first one will merge all models, finding in this way the elicitated parameters and in the second procedure, each alternate is classified (voted) by each separated model, and the majority vote decides the final class.

The authors have noted that concerning the voting procedure, nonlinear decision boundaries are generated and they can be suitable in analyzing problems of the same nature. In contrast, the merged model generates linear decision boundaries.

The elicitation of ELECTRE Tri-B parameters is made by an ensemble technique that is composed of a set of multicriteria models that are engaged in generating robust solutions.

This study aims to understand the impact that the experience environment has on the nightlife experience, as well as to identify the factors from the nightlifescape that most influence the tourists’ experience in Belgrade. Additionally, this study seeks to discover whether these factors changed after the outbreak of the COVID-19 pandemic.

To achieve the study’s objectives, 679 tourist reviews were collected from one of the most popular travel platforms, TripAdvisor, and analyzed using RapidMiner, the popular software for data/text mining.

The perception of the physical aspects of the experience environment, the presence of other tourists and the feeling of acceptance are identified as the key factors that influence tourists’ nightlife experience. This study also found that certain factors from the social and sensory environment, such as staff, the presence of other people, the atmosphere and music, had a positive impact on the tourist experience and their intention to recommend the nightlife experience in Belgrade. Moreover, it was discovered that the COVID-19 pandemic did not provoke changes in the main factors influencing tourists’ nightlife experience.

The perception of tourists about Belgrade during the night contributes to the growing body of tourism literature on destination image. Focusing solely on the perception of tourists about Belgrade during the night, this study adds a temporal determinant to the destination image, which can be considered as a valuable add on to the current knowledge in the field.

The purpose of this paper is to explore informational structures producing and organising the construction of waste sorting in Sweden. It shows how the issue is constructed by it being searched for in Google and how this contributes to the specific informational texture of waste sorting in Sweden. It is guided by the following questions: who are the main actors and which are the central topics featuring in Google results on popular, suggested searches for waste sorting in Sweden? What do the link relations between these tell the author about the issue space that is formed around waste sorting in Sweden? How is the construction of the notions of waste sorting and waste shaped in the information available through Google’s features for related and other relevant searches?

Waste sorting is discussed as a practice structured along moral rules and as a classification exercise. The study brings together two types of material, results from searches carried out in Google and lists of Google query suggestions for relevant search terms. These are analysed with a mixed method approach, uniting quantitative network analysis and qualitative content analysis of query suggestions. A sociomaterial approach theoretically grounds the analysis.

Waste sorting in Sweden emerges as an issue that is characterised by dense networks of rules and regulation, focused in public authorities and government agencies, which in turn address consumers, waste management businesses and other authorities. Search engine use and waste sorting in Sweden are shown to be joined together in various mundane everyday life practices and practices of governance that become visible through the search engine in form of search results and suggested searches. The search engine is shown to work as a fluid classification system, which is also created and shaped by its use.

The study offers a novel methodological approach to studying the informational structures of an issue and of its shaping through it being searched for. The sociomaterially grounded analysis of Google as a fluid classification system is original.

Current Army doctrine stresses a need for military leaders to have the capability to make flexible and adaptive decisions based on a future unknown environment, location and enemy. To assess a military decision maker’s ability in this context, this paper aims to modify the Wisconsin Card Sorting Test which assesses cognitive flexibility, into a military relevant map task. Thirty-four military officers from all service branches completed the map task.

The purpose of this study was to modify a current psychological task that measures cognitive flexibility into a military relevant task that includes the challenge of overcoming experiential bias, and understand underlying causes of individual variability in the decision-making and cognitive flexibility behavior of active duty military officers on this task.

Results indicated that non-perseverative errors were a strong predictor of cognitive flexibility performance on the map task. Decomposition of non-perseverative error into efficient errors and random errors revealed that participants who did not complete the map task changed their sorting strategy too soon within a series, resulting in a high quantity of random errors.

This study serves as the first step in customizing cognitive psychological tests for a military purpose and understanding why some military participants show poor cognitive flexibility.