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While scholars have researched the impact of specific total quality management (TQM) aspects on innovation performance (INP), empirical evidence on how the former, as a composite construct, influences the latter is rare. To add, empirical evidence on the mechanism through which TQM passes to affect innovative behaviour (INB) and, then, INP is limited. Essentially, scholars have requested that future research look at the boundary conditions that support the adoption of TQM activities in businesses. Although the banking sector has experienced a number of transformations, there is still a need to raise the standard of service provided to bank customers. This research sheds more light on this subject.

This research tests the hypotheses in Ahinful et al.’s (2023) conceptual model using responses from 260 top- and middle-level bank managers by applying Smart PLS. Organisational support and team member exchange were used as potential control variables for the mediator, while slack resources and bank size were applied to the target endogenous latent construct. Mediation and moderation effects were estimated using the variance accounted for (VAF) and product indicator approaches, respectively. Sig. level was set at 5%.

This study found that TQM and INP had a positive and significant connection (ß = 0.303, p = 0.000), and INB partially mediated this connection (VAF = 40.92%). However, government regulation (GOV; ß = 0.055, p = 0.365), market dynamism (MKD; ß = 0.063, p = 0.434), competitive intensity (CMP; ß = 0.069, p = 0.297) and technological turbulence (TUR; ß = 0.011, p = 0.865) all failed to moderate the TQM–INB connection, although the expected positive directions of these moderation relationships were established.

This research provides empirical evidence on the TQM–INP connection, how this connection may be mediated and how the TQM–INB connection may be activated. It also sheds light on novel ways in which service quality in the banking sector may be improved. Upcoming research may explore other control variables in their research. Since the moderating relationships were unsupported, this avenue is open for further research, particularly in other banking settings across the globe.

Practical lessons for bank consultants, regulators, customers, employees and managers are deliberated.

This research is novel. It is the first to test the hypotheses in Ahinful et al.’s (2023) conceptual model. This study advances the theoretical frameworks and existing knowledge within the TQM, innovation and performance management fields.

This study seeks to examine the relationship between macroeconomic shock indicators, namely geopolitical risk (GPR), global economic policy uncertainty (GEPU) and financial stress (FS), and returns as well as volatilities on seven carefully selected stock markets in Africa. Specifically, the study intends to unravel the co-movement and interdependence between the respective macroeconomic shock indicators and each of the stock markets under consideration across time and frequency.

This study employed wavelet coherence approach to examine the strength and stability of the relationships across different time scales and frequency components, thereby providing valuable insights into specific periods and frequency ranges where the relationships are particularly pronounced.

The study found that GEPU, Financial Stress (FS) and GPR failed to induce significant influence on African stock market returns in the short term (0–4 months band), but tend to intensify in the long-term band (after 6th month). On the contrary, stock market volatilities exhibited strong coherence and interdependence with GEPU, FSI and GPR in the short-term band.

This study happens to be the first of its kind to comprehensively consider how the aforementioned macro-economic shock indicators impact stock markets returns and volatilities over time and frequency. Further, none of the earlier studies has attempted to examine the relationship between macro-economic shocks, stock returns and volatilities in different crisis periods. This study is the first of its kind in to employ data spanning from May 2007 to April 2023, thereby covering notable crisis periods such as global financial crisis (GFC) and the COVID-19 pandemic episodes.