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The level of consumer satisfaction (CS), dissatisfaction (CD) and complaint behaviours (CCB) is beginning to receive increased attention from practitioners and researchers. Most studies, however, have tended to investigate the cause and/or effects of (dis)satisfaction or complaint behaviours. Relatively less work has been done to understand the relationship between dissatisfaction (CD) and complaint behaviours (CCB). This relationship is examined expirically. From a critical evaluation of previous research, hypotheses for the non‐linear relationship and the moderating role for CD and CCB are developed for an exploratory study. Using data on banking and financial services dissatisfaction, these hypotheses are investigated empirically. The results suggest that CD plays a broad and pervasive role (i.e. linear, non‐linear, moderator) depending upon the CCB dimension considered. Several implications of the results for managers are discussed and guidelines for dissatisfaction management are provided.

Deep learning (DL) is on the rise because it can make predictions and judgments based on data that is unseen. Blockchain technologies are being combined with DL frameworks in various industries to provide a safe and effective infrastructure. The review comprises literature that lists the most recent techniques used in the aforementioned application sectors. We examine the current research trends across several fields and evaluate the literature in terms of its advantages and disadvantages.

The integration of blockchain and DL has been explored in several application domains for the past five years (2018–2023). Our research is guided by five research questions, and based on these questions, we concentrate on key application domains such as the usage of Internet of Things (IoT) in several applications, healthcare and cryptocurrency price prediction. We have analyzed the main challenges and possibilities concerning blockchain technologies. We have discussed the methodologies used in the pertinent publications in these areas and contrasted the research trends during the previous five years. Additionally, we provide a comparison of the widely used blockchain frameworks that are used to create blockchain-based DL frameworks.

By responding to five research objectives, the study highlights and assesses the effectiveness of already published works using blockchain and DL. Our findings indicate that IoT applications, such as their use in smart cities and cars, healthcare and cryptocurrency, are the key areas of research. The primary focus of current research is the enhancement of existing systems, with data analysis, storage and sharing via decentralized systems being the main motivation for this integration. Amongst the various frameworks employed, Ethereum and Hyperledger are popular among researchers in the domain of IoT and healthcare, whereas Bitcoin is popular for research on cryptocurrency.

There is a lack of literature that summarizes the state-of-the-art methods incorporating blockchain and DL in popular domains such as healthcare, IoT and cryptocurrency price prediction. We analyze the existing research done in the past five years (2018–2023) to review the issues and emerging trends.