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This study aims to understand the extent of privacy concerns regarding voice-activated personal assistants (VAPAs) on Twitter. It investigates three key areas: (1) the effect of privacy-related press coverage on public sentiment and discussion volume; (2) the comparative negativity of privacy-focused conversations versus general conversations; and (3) the specific privacy-related topics that arise most frequently and their impact on sentiment and discussion volume.

A dataset of 441,427 tweets mentioning Amazon Alexa, Google Assistant, and Apple Siri from July 1, 2019 to June 30, 2021 were collected. Privacy-related press coverage has also been monitored. Sentiment analysis was conducted using the dictionary-based software LIWC and VADER, whereas text mining packages in R were used to identify privacy-related issues.

Negative privacy-related news significantly increases both negativity and volume in Twitter conversations, whereas positive news only boosts volume. Privacy-related tweets were notably more negative than general tweets. Specific keywords were found to either increase or decrease the sentiment and discussion volume. Additionally, a temporal evolution in sentiment, with general attitudes toward VAPAs becoming more positive, but privacy-specific discussions becoming more negative was observed.

This research augments the existing online privacy literature by employing text mining methodologies to gauge consumer sentiments regarding privacy concerns linked to VAPAs, a topic currently underexplored. Furthermore, this research uniquely integrates established theories from privacy calculus and social contract theory to deepen our analysis.

Despite the growing interest regarding the Brazilian rice industry, there is not much literature focusing on the role of the institutions in the process of technological capability accumulation and in the formation of the technological trajectories within this industry. This paper aims to discover the role of local institutions in the generation and dissemination of knowledge for creating the technological capability that can define different technological trajectories, using the Brazilian rice industry as an empirical field.

To achieve said objective, this paper uses secondary data (documental research) and a multiple case study design based on primary empirical evidence (content analysis and direct observation) about the Brazilian rice industry.

The paper’s main contribution is the empirical application of a framework that allows us to evaluate the institutions’ roles and activities and how these capabilities evolve as the firms’ technological levels progress and the technological trajectory is formed. Regarding aspects related to public policy, the authors found some implications that are mainly related to the need to consolidate this type of institution in developing countries with the goal of strengthening its technological capabilities, allowing these countries to operate on the technological boundary and to compete with developed countries.

There are few attempts to relate the technological capability, technological trajectories and institutions in the Brazilian rice industry. Therefore, to the best of the authors’ knowledge, the novelty of this study lies in the analysis of these theoretical approaches in this industrial sector, more specifically, in the Brazilian rice industry.

This study aims to explore human–machine interactions in the process of adopting artificial intelligence (AI) based on the principles of Taylorism and digital Taylorism to validate these principles in postmodern management.

The topic has been investigated by means of a case study based on the current experience of Carrozzeria Basile, a body shop born in Turin in 1970.

The Carrozzeria Basile’s approach is rooted in scientific management concepts, and its digital evolution is aimed at centring humans, investigating human–machine interactions and how to take advantage of both of these.

The research contributes to both Taylorism management and the literature on human–machine interactions. A unique case study represents a first step in comprehending the phenomenon but could also represent a limit for the study.

Practical implications refer to the scientific path to facilitate the implementation and adoption of emerging technologies in the organisational process, including employee engagement and continuous employee training.

The research focuses on human–machine interactions in the process of adopting AI in the automation process. Its novelty also relies on the comprehension of the needed path to facilitate these interactions and stimulate a collaborative and positive approach. The study fills the literature gap investigating the interactions between humans and machines beginning with their historical roots, from Taylorism to digital Taylorism, in relation to an empirical scenario.