Diagnosing Service Success and Failure Incidents in the Consumer-to-Business Sharing Economy

This study uses the critical incident technique to collect and analyze incidents of service failure and success involving a logistics sharing service in which the service providers are individuals. The authors also explore the key factors that affect customer satisfaction, along with the official and ideal recovery strategies. Data is based on interviews with 35 business users in Taiwan in 2017. A card sorting exercise is employed to classify the collected incidents and strategies into categories. The results show that the determinants of success and failure in logistics sharing services include drivers, platform operation, the matching system, and communication. Compensation is the most effective recovery strategy, whereas doing nothing is the least effective. Suggestions based on our results can help managers of the sharing economy to avoid or recover from failures and attain success.

Diagnosing Service Success and Failure Incidents in the Consumer-to-Business Sharing Economy

This study uses the critical incident technique to collect and analyze incidents of service failure and success involving a logistics sharing service in which the service providers are individuals. The authors also explore the key factors that affect customer satisfaction, along with the official and ideal recovery strategies. Data is based on interviews with 35 business users in Taiwan in 2017. A card sorting exercise is employed to classify the collected incidents and strategies into categories. The results show that the determinants of success and failure in logistics sharing services include drivers, platform operation, the matching system, and communication. Compensation is the most effective recovery strategy, whereas doing nothing is the least effective. Suggestions based on our results can help managers of the sharing economy to avoid or recover from failures and attain success.

Online service failure: how consumers from emerging countries react and complain

Purpose E-commerce has experienced huge growth in emerging countries, but analysis of service failure/recovery for online retailers has been limited in this context. Hence, this study aims to investigate customers’ reactions to service failures in e-commerce and the influence of cultural dimensions on complaint intentions. Design/methodology/approach A survey was conducted with 553 customers from Brazil, India and China. A model was proposed and tested using structural equation modeling. Findings Results indicate that satisfaction with service recovery (SSR) increases repurchase intentions and reduces third-party complaints and negative word-of-mouth. This study also shows that the reactions of consumers to service failures in online situations are influenced by their cultural orientation (i.e. individualism, uncertainty avoidance and power distance) and contingent factors (i.e. relationship level, switching costs and the severity of the failure). Originality/value This study shows that the extent to which consumers from emerging countries complain after a service failure in online purchases will depend on their cultural orientation, previous experiences with the retailer, switching costs and the severity of the failure.

Service Failure, Recovery, and Sustainable Development

Extant literature on corporate social responsibility (CSR) and marketing shows that CSR plays an important role when a service fails; thus, application of recovery strategy becomes crucial for sustainable development. CSR creates greater performance expectations amongst stakeholders as well as helps to legitimise organisational activities when a service fails. This study maintains that CSR is crucially important not only in legitimising organisational actions, but in ensuring that stakeholders' loyalty, trust, and justice are assured. This CSR, service failure, and recovery nexus is more needed in the controversial extractive industry in Nigeria, which has a history of illegitimacy, irresponsible corporate responsibility, lack of accountability, and failure of justice, which have triggered and sustained corporate-stakeholder conflict. This landscape has negative impact on sustainable development, peace, and justice in the Niger Delta region of Nigeria, where oil is extracted.

Evaluation of Machine Learning Methods for Monitoring the Health of Guyed Towers

This paper presents the development of a methodology to detect and evaluate faults in cable-stayed towers, which are part of the infrastructure of Brazil’s interconnected electrical system. The proposed method increases system reliability and minimizes the risk of service failure and tower collapse through the introduction of predictive maintenance methods based on artificial intelligence, which will ultimately benefit the end consumer. The proposed signal processing and interpretation methods are based on a machine learning approach, where the tower vibration is acquired from accelerometers that measure the dynamic response caused by the effects of the environment on the towers through wind and weather conditions. Data-based models were developed to obtain a representation of health degradation, which is primarily based on the finite element model of the tower, subjected to wind excitation. This representation is also based on measurements using a mockup tower with different types of provoked degradation that was subjected to ambient changes in the laboratory. The sensor signals are preprocessed and submitted to an autoencoder neural network to minimize the dimensionality of the resources involved, being analyzed by a classifier, based on a Softmax configuration. The results of the proposed configuration indicate the possibility of early failure detection and evolution evaluation, providing an effective failure detection and monitoring system.