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This paper aims to offer a comprehensive examination of the various solutions currently accessible for addressing the challenge of semantic interoperability in cyber physical systems (CPS). CPS is a new generation of systems composed of physical assets with computation capabilities, connected with software systems in a network, exchanging data collected from the physical asset, models (physics-based, data-driven, . . .) and services (reconfiguration, monitoring, . . .). The physical asset and its software system are connected, and they exchange data to be interpreted in a certain context. The heterogeneous nature of the collected data together with different types of models rise interoperability problems. Modeling the digital space of the CPS and integrating information models that support cyber physical interoperability together are required.

This paper aims to identify the most relevant points in the development of semantic models and machine learning solutions to the interoperability problem, and how these solutions are implemented in CPS. The research analyzes recent papers related to the topic of semantic interoperability in Industry 4.0 (I4.0) systems.

Semantic models are key enabler technologies that provide a common understanding of data, and they can be used to solve interoperability problems in Industry by using a common vocabulary when defining these models.

This paper provides an overview of the different available solutions to the semantic interoperability problem in CPS.

The purpose of this paper is to analyze the mediating effect of the identification of valuable external knowledge on the relationship between the development of inter-organizational ties (structural social capital) and the acquisition of external knowledge.

Using a sample of 87 firms from Spanish biotechnology and pharmaceutics industries, the authors have tested the proposed mediation hypothesis by applying the partial least squares technique to a structural equations model.

The study results show that those firms with stronger, more frequent and closer inter-relationships are able to increase the amount of intentionally acquired knowledge, partly due to the greater level of development of their knowledge identification capability. Thus, firms with a higher capability to recognize the value of the knowledge embedded in their inter-organizational networks will be more likely to design better strategies to acquire and integrate such knowledge into their current knowledge bases for either present or future use.

This research contributes to knowledge management and social capital literature by means of the study of two key determinants of knowledge acquisition – structural social capital and knowledge identification capability – and the explanation of their relationships of mutual influence. The paper thus tries to fill this literature gap and connects the relational perspective of social capital with the knowledge-based view from a strategic point of view.

Misinformation on social media has become a great threat across the globe. Therefore, the authors aim to provide a comprehensive understanding of social media users' misinformation combating behavior, especially during the COVID-19 pandemic. Specifically, the authors merged the uses and gratifications theory, social cognitive theory and theory of prosocial behavior into one theoretical framework (e.g. information seeking, status seeking, entertainment and norms of reciprocity) to understand their effect on users' prosocial media sharing experience and misinformation self-efficacy to combat misinformation.

The authors collected data from 356 social media users through “Google Forms” during the third wave of coronavirus in Pakistan. Further, the authors applied structural equation modeling for hypotheses testing.

The authors noted that entertainment and perceived norms of reciprocity positively affect social media users' prior experience and misinformation self-efficacy to enhance their misinformation combating intention. However, information seeking positively affects social media users' prior experience and insignificantly affects their misinformation self-efficacy. Similarly, status seeking was noted to be insignificantly associated with social media users' prior experience and misinformation self-efficacy.

The authors tested this model of misinformation combating intention in a developing country during the COVID-19 pandemic and noted that entertainment and status seeking motives are context-specific. Therefore, this study may likely benefit researchers, academicians and policymakers to understand the causal relationship between motivations and the behavior of combating misinformation on social media within a developing country.

In this study the authors merged three theories (e.g. uses and gratifications theory, social cognitive theory and theory of prosocial behavior) to understand information seeking, status seeking, entertainment and norms of reciprocity as the main motives for social media users' misinformation combating intention.