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Purpose This study aims to explore consumer motivations to mitigate the contagion effect in access-based consumption after instances of prior customer misbehavior. Reverse contagion, demonstrated through customer citizenship behavior, entails using both firm-provided and personal resources to cocreate value, even in the presence of norm violations by others. The research delves into the influence of empathy, narrative appeal and past misbehavior severity on customer behavior, specifically in the context of reverse contagion. Design/methodology/approach Two scenario-based studies and a field study were used within the context of scooter-sharing to assess the conceptual model. Study 1 (n = 156) and Study 2 (n = 97) were conducted through surveys. Study 3 (n = 54) was a field study. Findings The results emphasize the crucial role of empathy in breaking the cycle of misbehavior contagion. Specifically, the findings suggest that narrative appeals have the potential foster greater empathy, encouraging customers to counteract the contagion. However, the intensity of prior misbehavior lessens the efficacy of narrative appeals in triggering reverse contagion, thereby moderating the mediating effect of empathy. Originality/value This study investigates reverse contagion stemming from customer misbehavior in accessed-based consumption. It delves into the impact of empathy, narrative appeal and previous misbehavior on the dynamics of value codestruction and cocreation. This comprehensive examination of these factors within a unified framework represents a new contribution to the literature. The results illuminate this intricate phenomenon, offering valuable insights for managers to address adverse customer behavior and harness the positive aspects of reverse contagion.

Purpose Access-based services (ABSs) provide short-term access to goods, physical facilities, space or labor in exchange for access fees without transferring legal ownership (e.g. bike-sharing). This study aims to investigate what service providers can do to minimize financial losses when customers misbehave with the service providers’ assets in ABSs. The study also examines the effects of product misuse on subsequent customers and what factors may mitigate it. Design/methodology/approach The study uses a scenario-based experiment to test the conceptual model. Findings Injunctive norms reduce the mediating effect of descriptive norms on misbehavior contagion. As generally accepted and approved (injunctive) norms become salient, they override the impact of prevailing (descriptive) norms, thereby breaking the vicious cycle of misbehavior contagion. Customer-company identification (CCI) and reduced interpersonal anonymity mitigate the effects of previous misbehavior on misbehavior contagion. Practical implications ABS firms should strive to mitigate the financial and reputational losses they suffer from customer misbehavior. Such mitigation would be a win-win for the ABS firm (reduced misbehavior) and the customers (improved user experience). Originality/value The research complements prior research highlighting the role of social norms in misbehavior contagion. The study demonstrates the role of boundary conditions by investigating the interactive effects of descriptive and injunctive norms. In addition, it shows the positive impact of CCI and reduced interpersonal anonymity on containing misbehavior contagion.

This paper proposes routing misbehavior detection in MANETs using 2ACK scheme. Routing protocols for MANETs are designed based on the assumption that all participating nodes are fully cooperative. However, due to the open structure and scarcely available battery-based energy, node misbehavior may exist. In the existing system, there is a possibility that when a sender chooses anintermediate link to send some message to a destination, the intermediate link may pose problems such as, the intermediate node may not forward the packets to destination, it may take very long time to send packets or it may modify the contents of the packet. In MANETs, as there is no retransmission of packets once it is sent, care must be taken not to loose packets. We have analyzed and evaluated a technique, termed 2ACK scheme to detect and mitigate the effect of such routing misbehavior in MANETs environment. It is based on a simple 2-hop acknowledgment packet that is sent back by the receiver of the next-hop link. 2ACK transmission takes place for only a fraction of data packets, but not for all. Such a selective acknowledgment is intended to reduce the additional routing overhead caused by the 2ACK scheme. Our contribution in this paper is that, we have embedded some security aspects with 2ACK to check confidentiality of the message by verifying the original hash code with the hash code generated at the destination. If 2ACK is not received within the wait time or the hash code of the message is changed then the node to next hop link of sender is declared as the misbehaving link. We simulated the routing misbehavior detection using 2ACK scheme to test the operation scheme in terms of performance parameters.

This paper uses a natural field experiment to connect corporate social responsibility (CSR) to an important but often neglected behavior: employee misconduct and shirking. Through employing more than 1,500 workers, we find that our use of CSR increases employee misbehavior—24% more employees act detrimentally toward our firm by shirking on their primary job duties when we introduce CSR. Observed data patterns across the treatments are consonant with a model of “moral licensing,” whereby the “doing good” nature of CSR induces workers to misbehave on another dimension that is harmful to the firm. This paper was accepted by Yan Chen, decision analysis .

We investigate whether firms that proclaim a commitment to corporate social responsibility (CSR) by CSR reporting indeed internalize such a commitment and behave more responsibly. We analyze the association of the issuance and quality of voluntary CSR reports with the occurrence, number, and severity of corporate misbehaviors, both preceding and subsequent to CSR reporting. We find a significantly positive association of CSR reporting with our measures of prior and future misbehavior. The results are corroborated by a quasi-natural experiment around the Rana Plaza disaster where we find that the signatories of an accord for better working conditions have significantly higher prior and future misbehavior relative to non-signatories and firms unaffected by the exogenous shock. Our results are in line with legitimacy theory implying that, on average, the firms' proclaiming commitment to CSR is not a signal of internalized commitment but more likely serves greenwashing and impression management purposes.

Machine learning (ML) driven solutions have been widely used to secure wireless communications Vehicular ad hoc networks (VANETs) in recent studies. Unlike existing works, this paper applies support vector machine (SVM) for intrusion detection in VANET. The structure of SVM has many computation advantages, such as special direction at a finite sample and irrelevance between the complexity of algorithm and the sample dimension. Intrusion detection in VANET is nonconvex and combinatorial problem. Thus, three intelligence optimization algorithms are used for optimizing the accuracy value of SVM classifier. These optimization algorithms include Genetic algorithm (GA), Particle swarm optimization (PSO), and ant colony optimization (ACO). Our results demonstrate that GA outperformed other optimization algorithms.

Consumer indirect misbehavior in access-based consumption is a significant challenge for enterprises. The literature is in short of a deep understanding of the antecedent conditions of consumer indirect misbehavior in this context and limited by inconsistent findings, calling for developing a holistic and integrative theoretical framework. This study integrates three commonly used theoretical perspectives in the consumer misbehavior literature (i.e., deterrence, rational decision-making, and ethical decision-making) to present holistic archetypes of consumer indirect misbehavior formation. In accordance with this theoretical objective, we adopted an emerging approach for configurational analysis, i.e., fuzzy-set qualitative comparative analysis (fsQCA), to analyze the complex combinations of six influencing factors. We collected data using a scenario-based field survey of 264 experienced consumers of a popular bike-sharing service in China. The scenarios were developed based on relevant literature and a Delphi study. The fsQCA results reveal multiple configurations for high and low levels of consumer misbehavior intention. Specifically, perceived benefits and moral definition play important roles, while the effect of sanctions is highly dependent on other factors. These results lead us to derive three theoretical propositions for antecedent conditions leading to consumers' indirect misbehavior intentions in access-based consumption. This study enriches our understanding of the causes of misbehavior and provides novel insights for management practitioners to take appropriate countermeasures.

Misbehavior detection in vehicular ad hoc networks (VANETs) is performed to improve the traffic safety and driving accuracy. All the nodes in the VANETs communicate to each other through message logs. Malicious nodes in the VANETs can cause inevitable situation by sending message logs with tampered values. In this work, various machine learning algorithms are used to detect the primarily five types of attacks namely, constant attack, constant offset attack, random attack, random offset attack, and eventual attack. Firstly, each attack is detected by different machine learning algorithms using binary classification. Then, the new procedure is created to do the multi classification of the attacks on best chosen algorithm from different machine learning techniques. The highest accuracy in case of binary classification is obtained with Naïve Bayes (100%), decision tree (100%), and random forest (100%) in type1 attack, decision tree (100%) in type2 attack, and random forest (98.03%, 95.56%, and 95.55%) in Type4, Type8 and Type16 attack respectively. In case of new procedure for multi-classification, the highest accuracy is obtained with random forest (97.62%) technique. For this work, VeReMi dataset (a public repository for the malicious node detection in VANETs) is used.

Vehicular ad hoc networks (VANETs) play an important role as enabling technology for future cooperative intelligent transportation systems (CITSs). Vehicles in VANETs share real-time information about their movement state, traffic situation, and road conditions. However, VANETs are susceptible to the cyberattacks that create life threatening situations and/or cause road congestion. Intrusion detection systems (IDSs) that rely on the cooperation between vehicles to detect intruders, were the most suggested security solutions for VANET. Unfortunately, existing cooperative IDSs (CIDSs) are vulnerable to the legitimate yet compromised collaborators that share misleading and manipulated information and disrupt the IDSs’ normal operation. As such, this paper proposes a misbehavior-aware on-demand collaborative intrusion detection system (MA-CIDS) based on the concept of distributed ensemble learning. That is, vehicles individually use the random forest algorithm to train local IDS classifiers and share their locally trained classifiers on-demand with the vehicles in their vicinity, which reduces the communication overhead. Once received, the performance of the classifiers is evaluated using the local testing dataset in the receiving vehicle. The evaluation values are used as a trustworthiness factor and used to rank the received classifiers. The classifiers that deviate much from the box-and-whisker plot lower boundary are excluded from the set of the collaborators. Then, each vehicle constructs an ensemble of weighted random forest-based classifiers that encompasses the locally and remotely trained classifiers. The outputs of the classifiers are aggregated using a robust weighted voting scheme. Extensive simulations were conducted utilizing the network security laboratory-knowledge discovery data mining (NSL-KDD) dataset to evaluate the performance of the proposed MA-CIDS model. The obtained results show that MA-CIDS performs better than the other existing models in terms of effectiveness and efficiency for VANET.