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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.

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.

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.

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.

Access-based services (ABS) have been adversely impacted by the COVID-19 pandemic. This study aims to validate customer barriers to ABS focusing on the contamination barrier. As service employees’ presence violates the COVID-19 physical distancing norms and heightens contamination fear, this paper tests its effects on continued use intentions of ABS. This study also empirically examines the role of conspicuous virtue signaling and organizational response.

The study was conducted in the context of scooter sharing and uses a mixed-method to explore the relatively under-researched problem of contamination fear in ABS. Study one uses the survey method and study two uses a 2 × 2 matrix between-subject design.

The results reveal that perceptions of resource sufficiency positively affect continued use intentions of ABS. The presence of a service employee hurts continued use intentions. Further, there are a three-way interaction between ABS type (service employee presence/absence), organizational response (solution-oriented/general information) and resource sufficiency perceptions. Organizational response mitigates the negative effect of service employee presence on the link between resource adequacy perceptions and ABS continued use intentions.

In contrast with prior research, this study shows that contamination fear invokes protection motivation, resulting in better preparedness and continued use intentions of ABS. The predicted difference is primarily between customers who attribute responsibility and ABS type differently (presence/absence of service employee). This study also explores the role of organizational response to COVID-19.

This study examines the impact of the brand's corporate social responsibility (CSR) on consumers' brand experience and, in turn, consumers' intention of word-of-mouth (WoM). The study also investigates customer satisfaction with service recovery (CSATR) as a boundary condition for the proposed relationship.

The study used a survey-based research design to test the conceptual model. A structured questionnaire was distributed to the participants through a survey link. A total of 276 valid responses were used and analysed using Smart PLS3, a structural equation modelling technique.

Results of this study demonstrate that perceived CSR have a positive impact on brand experience which eventually affects the WoM intention. Also CSATR moderates the relationship between brand experience and WoM such that satisfaction (dissatisfaction) with service recovery enhances (reduces) the effect of brand experience on WoM.

Managers can use CSR as a communication tool to enhance consumers' brand experience and focus on service recovery as a successful service recovery can enhance WoM over and above CSR.

To the best of the authors' knowledge, this is the first study to demonstrate brand experience as a mediator of CSR and WoM from the lens of signalling theory. The study also tries to address the scepticism of the consumers related to WoM for a brand's CSR by incorporating the service aspect into the CSR.

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.

The study uses a scenario-based experiment to test the conceptual model.

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.

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).

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 study aims to investigate the extent to which consumers recognize participation in consumption tax evasion (CTE) as an ethical issue and the impact of moral recognition on consumer intention to participate. It also explores the role of religiosity and attitude toward government in the ethical decision-making process of consumers.

The model was tested using the structural equation modeling approach over 128 responses collected through the mail survey.

The results of the study suggest that attitude towards government and religiosity influences the consumers’ intention to participate in CTE. The negative attitude toward government enhances the intention to participate in CTE. Religiosity negatively influences the intention to participate in CTE and makes people recognize participation in CTE as a moral issue.

There is a need to explore the impact of other factors such as social norms and personality traits on the consumer decision-making to participate in CTE.

This study indicates that the even when people recognize participation in CTE as morally wrong, they do not have intentions to avoid such behavior because of their attitude toward government. Governments need to improve their image among consumers to reduce CTE. Also, there is a need to launch social marketing campaigns to move consumers from just recognizing moral issues in CTE to forming intentions to resolve those issues.

Earlier studies have explored the moral issue related to tax evasion in a context where an individual has directly participated in such an act, whereas this study explores the ethical aspect in the situation where consumers play an indirect role in tax evasion, i.e. CTE. In the context of participation in CTE, this study points out that the consumers recognize their indirect participation in CTE as a moral issue, but they lack the intention to curb such behavior. Further, no earlier study has explored the impact of religiosity and attitude toward government in such indirect participation in tax evasion.

The purpose of this paper is to propose an approach that helps in acquisition of live data as well as data stored in the internal/external memory of android mobile device considering that the data on the device are not much altered during the extraction process. Also, the emphasis is laid on testing the validity of existing forensic tools against the data obtained manually and by using this approach. Smartphones have spurred the mobile computing technology, and Android is widely used as an Operating System in these devices. These days, users store most of their personal information like emails, images, contacts etc., on Phones/Tablets as their data would be readily accessible and thus convenient for them.

Android Operating System is built on the Linux Kernel and scripts to extract data from Android Mobile Device with the use of Android Debugging Bridge have been written. The approach is more focused on the logical acquisition of data from devices rather than acquisition using physical methods.

Live data of the Facebook application running on the device can be extracted. Also, the password of the LuksManager application (used to create an encrypted volume on the device), which is stored in the internal memory, is also extracted and identified.

The study has been conducted in an academic environment, thereby limiting external validity. Another limitation is the limited edition of some of the software forensics tools that are used. The full access to these software tools are restricted by Law enforcement and Investigation policies. The research provides a different approach which could aid in criminal investigation activities on mobile devices.

The devices which have the latest versions of Android not only store messages and mails, but a lot of information about GPS, as well as information about popular applications like Facebook, WhatsApp, etc. This could practically help a lot in criminal investigation.

This study is important because very few works have been done on recent versions (Jellybean and Kitkat) of Android. The proposed approach could extract large amounts of information as compared to earlier approaches with the newer versions of Android having larger memory and new features.

The focus is on how agencies can mitigate client opportunism in an agency-client relationship (ACR), particularly during the agency selection stage involving a pitch. This paper aims to empirically investigate the moderating effects of organizational mechanisms (particularly informational cues) and the agency’s past behavior on client opportunism. In a moderated moderation, this paper tests the effects of calculative commitment, informational cue and agency’s past behavior on the main effect.

The research is in the context of ACR involving a pitch at the agency selection stage. A mixed-method approach is used. In depth interviews with senior level executives were used to design the experimental vignettes. The main study uses experimental vignettes in a survey.

The study finds the prevalence of client opportunism during the pitch. The study reveals a significant relationship between information asymmetry and client opportunism. The findings of the study support the effectiveness of organizational mechanisms in mitigating client opportunism. The findings indicate that a proactive approach such as using informational cues mitigates client opportunism as it signals to the client that the agency cares for its intellectual property. Clients also take a cue from agencies past behavior. Third-party complaints and voice complaint deters client opportunism. Moderated moderation reveals that the client’s calculative commitment impacts client opportunism.

The study is novel in empirically examining client opportunism during the agency selection stage involving a pitch. The study re-emphasizes that information asymmetry is the primary reason for client opportunism in ACR at the agency selection stage. The role of organizational mechanism and agency response in mitigating client opportunism is a welcome addition. Moderated moderation effects involving calculative commitment is a novel addition.

Artificial neural network (ANN) is a powerful technique to forecast the time series data such as the stock market. Therefore, this study aims to predict the Indian stock market closing price using ANNs.

The input variables identified from the literature are some macroeconomic variables and a global stock market factor. The study uses an ANN with Scaled Conjugate Gradient Algorithm (SCG) to forecast the Bombay Stock Exchange (BSE) Sensex.

The empirical findings reveal that the ANN model is able to achieve 93% accuracy in predicting the BSE Sensex closing prices. Moreover, the results indicate that the Morgan Stanley Capital International world index is the most important variable and the index of industrial production is the least important in predicting Sensex.

The findings of the study have implications for the investors of all categories such as foreign institutional investors, domestic institutional investors and investment houses.

The novelty of this study lies in the fact that there are hardly any studies that use ANN to forecast the Indian stock market using macroeconomic indicators.

This study aims to predict the Indian stock market (Nifty 50) by employing macroeconomic variables as input variables identified from the literature for two sub periods, i.e. the pre-coronavirus disease 2019 (COVID-19) (June 2011–February 2020) and during the COVID-19 (March 2020–June 2021).

Secondary data on macroeconomic variables and Nifty 50 index spanning a period of last ten years starting from 2011 to 2021 have been from various government and regulatory websites. Also, an artificial neural network (ANN) model was trained with the scaled conjugate gradient algorithm for predicting the National Stock exchange's (NSE) flagship index Nifty 50.

The findings of the study reveal that Scaled Conjugate Gradient (SCG) algorithm achieved 96.99% accuracy in predicting the Indian stock market in the pre-COVID-19 scenario. On the contrary, the proposed ANN model achieved 99.85% accuracy in during the COVID-19 period. The findings of this study have implications for investors, portfolio managers, domestic and foreign institution investors, etc.

The novelty of this study lies in the fact that are hardly any studies that forecasts the Indian stock market using artificial neural networks in the pre and during COVID-19 periods.