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This study aims to present how a historical governance mechanism (a statutory rule of profit allocation) could answer the practical question of profit allocation, thereby proposing a methodology to enhance future quantitative studies.

The rule sets profit allocations to a predetermined set of stakeholders in corporate charters. It could be seen as a tool used by historical organisations to enact corporate social responsibility (CSR). The authors propose a straightforward way to calculate the payout ratios promised by this rule to each stakeholder. This methodology was applied to shareholders and used to calculate the promised dividend payout ratios.

This rule constitutes a natural experiment from which modern organisations could learn to implement the most relevant profit-allocation schemes given their CSR strategy. The authors propose calculating a promised payout ratio that would allow scholars to empirically examine the rule and its effects and provide accurate recommendations to these organisations.

This mechanism allows the study of profit allocations made to stakeholders (not limited to shareholders or employees like it is usually done). The promised payout ratio makes future quantitative investigations possible.

Modern organisations could use the CSR mechanism to allocate profits continuously in formats that would best fit their strategy and environment.

To the best of the author’s knowledge, this is the first article to examine the statutory rule of profit allocation per se, which proposes a new methodology to calculate payout ratios promised by the rule. The idea is to investigate their impact and provide recommendations for modern organisations to adapt.

This study aims to identify and explore different risk typologies associated with consumer acceptance of purchase recommendations from voice assistants (VAs).

Drawing on components of perceived risk, consumer trust theory, and consumption value theory, a research model was proposed and tested using structural equation modeling (SEM) with data from 482 voice shoppers.

The results reveal that, unlike risks associated with physical harm, privacy breaches, and security threats, a variety of other concerns—including financial, psychological, social, performance-related risks, time loss, and the overall perceived risks—significantly influence consumers' willingness to accept VAs purchase recommendations. The effect is mediated by trust in VA purchase recommendations and their perceived value. Different types of risk affect various consumption values, with functional value being the most influential. The model explains 58.6% of the variance in purchase recommendation acceptance and significantly elucidates the variance in all consumption values.

This study contributes crucial knowledge to understanding consumer decision-making processes as they increasingly leverage AI-powered voice-based dialogue platforms for online purchasing. It emphasizes recognizing diverse risk typologies associated with VA purchase recommendations and their impact on consumer purchase behavior. The findings offer insights for marketing managers seeking to navigate the challenges posed by consumers' perceived risks while leveraging VAs as an integral component of modern shopping environments.