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Dysfunctional customer behavior (DCB) is costly and problematic for organizations. This research seeks to understand how DCB spreads and how businesses can effectively deal with it through employee intervention.

This research conducted a survey study and an experimental study to examine the proposed model.

Through two studies, we discovered that when an employee intervenes to stop DCB and is perceived as having high coping ability, observing customers learn from the employee’s action, resulting in reduced empathy toward the dysfunctional customer and diminished intentions to engage in DCB. Conversely, if they perceive the employee as having low coping ability, the intervention backfires, enhancing the observers’ empathy toward the dysfunctional customer and consequently leading them to engage in more DCB.

This research unveils an additional mechanism that explains the spread of DCB. It also contributes to the employee intervention literature by shedding light on when employee intervention can backfire. Further, our application of social learning theory along with the person-situation interaction literature offers a fresh perspective in explaining service exchanges.

The purpose of this paper is to investigate how and when used by employees influences witnessing customers’ willingness to spread positive word of mouth (WOM).

This research used a qualitative method to develop a typology of necessary evil using two pilot studies and an experimental study to test the theoretical model.

The results show that the necessary evil used by employees to manage dysfunctional customers positively influences witnessing customers’ perceptions of distributive, procedural and interactional justice and their subsequent deontic justice perceptions, resulting in their willingness to spread positive WOM. Moreover, the positive influence of necessary evil on witnessing customers’ responses is strengthened when dysfunctional customer behavior (DCB) targets another customer as opposed to an employee.

This research offers service providers a better understanding of how to manage DCBs.

This paper contributes to the existing literature by introducing necessary evil to the service literature, proposing a new typology of employee response strategies to DCB based on necessary evil and examining how necessary evil drives positive customer responses. Additionally, it is among the first to examine the relationship between deontic justice and traditional justice mechanisms.

Twin support vector machine (TSVM) is an effective machine learning technique. However, the TSVM model does not consider the influence of different data samples on the optimal hyperplane, which results in its sensitivity to noise. To solve this problem, this study proposes a twin support vector machine model based on fuzzy systems (FSTSVM).

This study designs an effective fuzzy membership assignment strategy based on fuzzy systems. It describes the relationship between the three inputs and the fuzzy membership of the sample by defining fuzzy inference rules and then exports the fuzzy membership of the sample. Combining this strategy with TSVM, the FSTSVM is proposed. Moreover, to speed up the model training, this study employs a coordinate descent strategy with shrinking by active set. To evaluate the performance of FSTSVM, this study conducts experiments designed on artificial data sets and UCI data sets.

The experimental results affirm the effectiveness of FSTSVM in addressing binary classification problems with noise, demonstrating its superior robustness and generalization performance compared to existing learning models. This can be attributed to the proposed fuzzy membership assignment strategy based on fuzzy systems, which effectively mitigates the adverse effects of noise.

This study designs a fuzzy membership assignment strategy based on fuzzy systems that effectively reduces the negative impact caused by noise and then proposes the noise-robust FSTSVM model. Moreover, the model employs a coordinate descent strategy with shrinking by active set to accelerate the training speed of the model.

The purpose of this study is to address the challenge of task allocation in multi-robot systems by getting the minimum overall task completion time and task allocation scheme while also minimizing robot energy consumption. This study aims to move away from traditional centralized methods and validate a more scalable distributed approach.

This paper proposes a distributed algorithm for the multi-robot task allocation problem, aimed at getting the minimum task completion time along with the task allocation scheme. The algorithm operates based on local interaction information rather than global information. By using the Consensus-Based Auction Algorithm (CBAA), it seeks to effectively minimize energy consumption without affecting the minimum completion time required for overall task allocation.

The proposed distributed algorithm successfully reduces robot energy consumption while effectively obtaining the shortest overall task completion time and corresponding task allocation scheme. Numerical simulations conducted using MATLAB software demonstrated its superior performance, and empirical testing on the Turtlebot3-Burger robot platform further substantiated these findings.

The original contribution of this study lies in the development of an enhanced distributed task allocation strategy using CBAA to improve efficiency in multi-robot environments. Its value extends to applications that require rapid and resource-aware coordination, such as automated logistics or search-and-rescue operations.

This paper aims to propose a diverless weld bead maintenance welding technology to prevent the leakage of subsea oil and gas pipeline and solve the key problems in the maintenance of subsea pipeline.

Based on the analysis of the cross-section of the fillet weld, the multi-layer and multi-pass welding path planning of the submarine pipeline sleeve fillet weld is studied, and thus a multi-layer and multi-pass welding path planning strategy is proposed. A welding seam filling method is designed, and the end position of the welding gun is planned, which provides a theoretical basis for the motion control of the maintenance system.

The trajectory planning and adjustment of multi-layer and multi-pass fillet welding and the motion stability control of the rotating mechanism are realized.

It provides the basis for the prototype design of the submarine pipeline maintenance and welding robot system, and also lays the foundation for the in-depth research on the intelligent maintenance system of submarine pipeline.

The maintenance of diverless subsea pipeline is a new type of maintenance method, which can solve the problem of large amount of subsea maintenance work with high efficiency.

In the current economic scenario characterized by turbulence, innovation is a requisite for company's growth. The innovation activities are implemented through the realization of innovative project. This paper aims to prospect the promising opportunities coming from the application of Machine Learning (ML) algorithms to project risk management for organizational innovation, where a large amount of data supports the decision-making process within the companies and the organizations.

Moving from a structured literature review (SLR), a final sample of 42 papers has been analyzed through a descriptive, content and bibliographic analysis. Moreover, metrics for measuring the impact of the citation index approach and the CPY (Citations per year) have been defined. The descriptive and cluster analysis has been realized with VOSviewer, a tool for constructing and visualizing bibliometric networks and clusters.

Prospective future developments and forthcoming challenges of ML applications for managing risks in projects have been identified in the following research context: software development projects; construction industry projects; climate and environmental issues and Health and Safety projects. Insights about the impact of ML for improving organizational innovation through the project risks management are defined.

The study have some limitations regarding the choice of keywords and as well the database chosen for selecting the final sample. Another limitation regards the number of the analyzed papers.

The analysis demonstrated how much the use of ML techniques for project risk management is still new and has many unexplored areas, given the increasing trend in annual scientific publications. This evidence represents an opportunities for supporting the organizational innovation in companies engaged into complex projects whose risk management become strategic.

Starting from the premises that Internet of Things (IoT) applications can be used in museums as an aid to visiting systems, the purpose of this paper is to see how recommendation systems can be developed to provide advanced services to museum visitors.

The research methodology employs a qualitative exploratory multi-case study: the method used has consisted in crossing the information currently known on the most advanced communication technologies (ICT) with the requirements of enhancing museum services, in order to determine the possible trajectories of applying the former to the latter.

The implementation of recommender system outlines the main implications and effects of an advanced market-driven digital orientation, as the system’s users are the starting point for innovation and the creation of value. For a museum, it will be possible to access to an additional system of knowledge alongside that of its scientific staff. This process has profound implications in the way in which a museum presents itself and how it is perceived by its visitors and, in a wider sense, by the potential demand.

The paper consists in an exploratory effort to introduce an analytical framework for an evolved adaptive museum orientation system; the empirical investigation can be structured in the inductive-predictive view of assessing this promising debate further.

Implementing the IoT blueprint entails introducing a plethora of new products, services and business models, opening new routes to guide and direct cultural events. Now, more than ever, sustainable development involves an intrinsic balancing act between the pluralism of data and that of customer needs, which is achieved through the elaboration of digital data.

With the increasing spread of ontologies in various domains, units have gradually become an essential part of ontologies and units ontologies have been developed to offer a better expression ability for the practical usage. From the perspectives of architecture, comparison and reuse, the purpose of this paper is to provide a comprehensive survey on four mainstream units ontologies: quantity-unit-dimension-type, quantities, units, dimensions and values, ontology of units of measure and units ontology (UO) of the open biomedical ontologies, in order to address well the state of the art and the reuse strategies of the UO.

An architecture of units ontologies is presented, in which the relations between key factors (i.e. units of measure, quantity and dimension) are discussed. The criteria for comparing units ontologies are developed from the perspectives of organizational structure, pattern design and application scenario. Then, the authors compare four typical units ontologies based on the proposed comparison criteria. Furthermore, how to reuse these units ontologies is discussed in materials science domain by utilizing two reuse strategies of partial reference and complete reference.

Units ontologies have attracted high attention in the scientific domain. Based on the comparison of four popular units ontologies, this paper finds that different units ontologies have different design features from the perspectives of basis structure, units conversion and axioms design; a UO is better to be applied to the application areas that satisfy its design features; and many challenges remain to be done in the future research of the UO.

This paper makes an extensive review on units ontologies, by defining the comparison criteria and discussing the reuse strategies in the materials domain. Based on this investigation, guidelines are summarized for the selection and reuse of units ontologies.