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This study aims to identify and analyse the gains and strains associated with flexible working practices (FWPs) introduced and adopted prior to and during the pandemic and consider how these experiences are likely to shape the future of workplace flexibility post-pandemic.

A case study research strategy was adopted to explore the FWPs implemented by a state-owned organisation in the Chinese publishing industry. A mixed data collection method was used. Quantitative data was collected from 50 valid questionnaires, which was followed by 7 qualitative interviews to gain rich insights into the availability and effectiveness of various FWPs and associated benefits and drawbacks.

While the results confirm positive effects FWPs have on employee engagement and retention and on business continuity and employee well-being during the pandemic, the empirical analysis highlights the performance-driven patterns in use and impacts of some FWPs, which caused concerns and dilemmas. Besides the increasing intense market competition, the changing face of state-owned enterprises and managerial attitudes have been found to have significant effects on the use of FWPs.

This paper has contributed to a better understanding of flexible working in an under-researched setting, reflected in changes before and during the pandemic, offering an insight into the commercialised nature of flexible working in the Chinese context. It has implications for organisations and HR practitioners as they envision future workplace flexibility.

The sharing economy has evolved as a result of the diffusion of information and communication technology and facilitates collaborative consumption and production otherwise known as value co-creation. The present research aims to explore the consumer responses to value co-creation in sharing economy such as satisfaction, brand preference and enduring buyer–platform relationships, amid consumer's CSR concerns.

Drawing on the sharing economy and value co-creation literature and rooted in the stimulus-organism-response framework, an online panel data provider was employed to recruit 393 actual sharing economy consumers from the United States. Empirical analyses are performed using structural equation modeling through Amos, version.27.

Findings confirm that value co-creation intentions contribute to consumers' satisfaction, brand preference and sustainable social relationships in the sharing economy. As expected, heightened concerns of corporate social responsibility (CSR) led to decreased consumer satisfaction with the sharing economy platform.

The study contributes to the digital sharing economy literature by emphasizing the role of CSR perceptions for building long-term relationships (buyer–platform relationships) where value co-creation is crucial.

All hypothesized relationships were significant. Specifically, H1 was supported as disconfirmation and surprising consumption were significantly correlated. Moreover, arousal (H2) and outrage (H4a) were functions of surprising consumption and negative affect (H3) and outrage (H4b) were functions of arousal. H4c was also supported as negative affect had a significant direct effect on consumer outrage. In addition, disconfirmation had negative direct effects on both negative affect (H5) and dissatisfaction (H6a) and dissatisfaction was a function of negative affect. Finally, both outrage (H7a) and dissatisfaction (H7b) had significant negative effects on behavioral intentions.

Respondents were recruited to participate in the data collection in a “college town” in the Southeastern United States. Respondents were provided a paper and pencil data collection instrument that include complete survey instructions and the balance of the research design. To adequately test all hypotheses, the researchers developed a unique scenario that described an extreme service failure that takes place during a hotel check-in. Each respondent was asked to read the scenario and then reflect upon it as they responded to items that assessed their feelings toward the hotel check-in experience.

The results provide additional evidence in support of the existence of the satisfaction-dissatisfaction continuum, as well as specifically identifying the affective nature of levels of satisfaction that fall surprisingly well-below the zone of tolerance. The authors feel that the present study is a necessary step to provide a more comprehensive view of the satisfaction-dissatisfaction continuum. Likewise, the authors posit initial evidence of the antecedents and consequences of consumer outrage. This research supports the prior assumptions of Westbrook (1987) about the vast detrimental effects of negative affective responses to service or product failures.

Future research needs to discover just how extremely deficient service has to be to elicit outrage. Is outrage a personal phenomenon with every consumer experiencing it to different degrees? As such, is outrage triggered at different points on the satisfaction-dissatisfaction continuum? The zone of tolerance seems to suggest this, but it would be interesting to discover if at some collective level of dissatisfaction consumers generally begin to show signs of outrage. Likewise, it would be interesting to understand how the level and pattern of outrage results in customers exiting the relationship but also results in loyal customers becoming enemies (e.g. Gregiore et al., 2009; Gregiore and Fisher, 2008).

The motivation for the current study is both pragmatic and theoretical. As alluded to above, it is evident that the level of service customers’ emotional responses to their service experiences are increasing in frequency and intensity. These negative emotions affect the efficacy of service workers and impede the financial performance of service providers. The popular mantra of “anti-woke” consumers, “Go Woke, Go Broke,” is indicative of the importance of negative emotion. Sometimes referred to as “brand activism” (Moorman, 2020; Sarkar and Kotlet, 2019), recent public stances on social and political issues have led to a boycott of Gillette razors, the burning of Nike shoes, and the canceling of Costco Memberships in what has been called “virtue signaling” (Vredenburg et al., 2020). While none of these actions are desirable, the importance of investigating the impact of strong negative emotions (i.e. outrage) is further demonstrated in reports that 65% of consumers expect companies to authentically support such issues (Barton et al., 2018; Edelman, 2018; Larcker and Tayan, 2018; Moorman, 2020).

The present work has proposed and employed an innovative hybrid method based on the combination of factor analysis and an artificial neural network (ANN) model to forecast customer satisfaction from the identified dimensions of service quality in India, a developing country.

The qualitative study is conducted with Internet banking users to understand e-banking clients' perceptions. The data is collected with the help of a questionnaire from randomly selected 208 customers in India. Firstly, factor analysis was performed to determine the influential factors of customer satisfaction, and four factors i.e. efficiency, reliability, security and privacy, and issue and problem handling were extracted accordingly. The neural network model is then applied to the factor scores to validate the key elements. Lastly, the comparative analysis of the actual ANN and the regression predicted result is done.

The success ability of the linear regression model is challenged when approximated to nonlinear problems such as customer satisfaction. It is concluded that the ANN model is a better fit than the linear regression model, and it can recognise the complex connections between the exogenous and endogenous variables. The results also show that reliability, security and privacy are the most influencing factors; however, problem handling and efficiency have the slightest effect on bank client satisfaction.

This research is conducted in India, and the sample is chosen from the urban area. The limitation of the purposeful sampling technique and the cross-sectional nature of the data may hamper the generalisation of the results.

The conclusions from the study will be helpful for policymakers, bankers and academicians. To our knowledge, few studies used ANN modelling to predict customer satisfaction in the service sector