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Starting from intention–behaviour models and building upon the growing evidence that aggregated internet search query data represent a good proxy of human interests and intentions. The purpose of this study is to demonstrate that the internet search traffic information related to the selected key terms associated with establishing new businesses, reflects well the dynamics of entrepreneurial activity in a country and can be used for predicting entrepreneurial activity at the national level.

Theoretical framework is based on intention–behaviour models and supported by the knowledge spillover theory of entrepreneurship. Monthly data on new business registration from 2018 to 2021 is derived from the open database of the Russian Federal Tax Service. Terms of internet search interest are identified through interviews with the recent founders of new businesses, whereas the internet search query statistics on the identified terms are obtained from Google Trends and Yandex Wordstat.

The results suggest that aggregated data about web searches related to opening a new business in a country is positively correlated with the dynamics of entrepreneurial activity in the country and, as such, may be useful for predicting the level of that activity.

The results may serve as a starting point for a new approach to measure, monitor and predict entrepreneurial activities in a country and can help in better addressing policymaking issues related to entrepreneurship.

To the best of the authors’ knowledge, this study is original in its approach and results. Building on intention–behaviour models, this study outlines, to the best of the authors’ knowledge, the first usage of big data for analysing the intention–behaviour relationship in entrepreneurship. This study also contributes to the ongoing debate about the value of big data for entrepreneurship research by proposing and demonstrating the credibility of internet search query data as a novel source of quality data in analysing and predicting a country’s entrepreneurial activity.

This study examines whether different strategy archetypes deploy specific performance metrics to support their strategic goals and priorities. If so, does alignment of strategy and metrics positively impact organisational performance?

The conceptual framework and hypotheses are couched in Contingency Theory. The role of business strategy as a moderating variable is tested using MANOVA, followed by post hoc pairwise comparisons. The results are based on cross-sectional survey data from 372 manufacturing and service organisations in Italy.

The overall contingency effect of business strategy in selecting and deploying performance metrics and their effect on organisational performance is supported. However, the group-wise post hoc analyses show support only for Prospectors but not for Defenders and Analysers.

This research lends further support in favour of the Contingency Theory from a new geographic context (Italy) that there are no universally best performance metrics that drive organisational performance. However, more research is needed to understand why the theory only holds for certain strategic archetypes and not across all archetypes.

Managers can direct resources and effort towards designing and deploying the “right” type of performance metrics suitable for their strategic orientation and thus optimise organisational performance.

This is a rare study that tests the moderating role of business strategy using all four strategic archetypes of the Miles and Snow typology. It deploys both financial and non-financial measures and uses a very large sample of both manufacturing and service organisations from a relatively unexplored region of the world. The study provides additional evidence in favour of the Contingency Theory whilst advocating for more research to refine our understanding of why the contingency perspective is not so important for firms that are not the first-in.