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This paper aims to systematically unpack the ideal of organizational transparency by tracing the concept's origins in the era of Enlightenment. Based on a genealogical reconstruction, the article explores different transparency understandings in key areas of online public relations (PR) and discusses the opportunities and challenges they present for the field.

This is a conceptual paper that unfolds a genealogical reconstruction to uncover different transparency ideals of modernity. These perspectives are then transferred to the field of online PR to discuss their ethical and practical implications in the context of digitalization.

Claims for transparency manifest in three distinct ideals, namely normative, instrumental and expressive transparency, which are also pursued in online PR. These ideals are related to associated concepts, like dialogue, control and authenticity, which serve as transparency proxies. Moreover, each transparency ideal inherits an ambivalence that presents unique opportunities and challenges for PR practitioners.

Instead of an unquestioned belief in the ideal of organizational transparency, the paper urges communication practitioners to critically reflect on the ambivalent nature of different transparency regimes in the context of digitalization and provides initial recommendations on how to manage digital transparency in online PR responsibly.

The paper contributes to the vivid debate surrounding organizational transparency in the context of digitalization by offering a novel and systematic analysis of the multifaced concept of transparency while opening new research avenues for further conceptual and empirical research.

This work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.

The proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.

The proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.

Thanks to the abnormal risk panel; human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.

The monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products’ waste avoidance.

Digital twins (DTs) and other data-based solutions are gaining an increasing foothold in manufacturing business, whereas a mere physical product is often insufficient to satisfy all customers’ expectations. As a result, companies are seeking novel ways of value creation, and one exciting opportunity is the use of DTs in new business creation, where they can offer diverse possibilities for innovative businesses. This paper aims to examine the impacts and challenges of DTs on new business creation in the manufacturing industry.

This study used a qualitative research approach, which combined semistructured interviews and an iterative Delphi study as research methods. The participants for the interviews and Delphi study were from different sectors and roles in the manufacturing industry. Altogether, 10 interviewees from eight companies took part in the interviews, and the expert panel of the Delphi method contained 12 professionals.

The results of the study indicated that DT can significantly impact the business models of manufacturing companies. DT can enhance operations, offer cost savings and business growth and allow stakeholders to focus on core competencies while developing their businesses. Several challenges for leveraging DT were identified, such as data ownership, resource allocation, internal bureaucracy and the difficulty of demonstrating the actual value of data-based services to potential customers.

This paper provides a structured expert-led assessment of the potential impacts of DT utilization in the creation of new business opportunities.