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The purpose of this paper is to evaluate the effectiveness of a policy, the 2014 UK Supply Chain Plan, which aimed to create a local supply chain in a sector (offshore wind) where the central manufacturing node capabilities and knowledge are not possessed locally. It aims to address the following question: can policy create a new manufacturing supply chain from a void?

A qualitative field research approach is used to derive an original set of theoretical propositions explaining the motivation behind the Supply Chain Plan policy. The outcomes of this policy are examined 10 years later in order to provide an opportunity to observe the impact of the policy.

The conclusion is that the policy has been successful in increasing local content, and some of that local content has benefited local manufacturers. However, a lot of the increase in local content has been achieved in non-manufacturing areas or in new areas. The main issue, i.e. the lack of a central manufacturing capability, remains unaddressed. The impact of the local content policy on cost is undocumented.

There is an increasing amount of interest in regional/local supply chains after the COVID-19 pandemic and increasing geopolitical tensions. This paper's originality is to document with an industry case study the fact that manufacturing knowledge and capabilities are the central growth engine of supply chains and that creating a new manufacturing supply chain competing with well-established clusters is not a simple matter that can be achieved through a local content policy. Such policies raise critical questions about the policy makers' implicit valuation of manufacturing technology.

In emergency departments, effective communication is of utmost importance to ensure the safety of patients. However, communicating can be quite challenging when dealing with high-stress situations. This study aims to assess the efficacy of coaching workshops, informed by a transtheoretical coaching model, in managing communication challenges perceived by emergency nurses and enhancing their communication skills.

The study involved seven emergency room nurses working at a public hospital in Morocco. The data were gathered through various instruments, including observation grids, interviews and pre- and post-test questionnaires.

The study identified prevalent challenges in communication among nurses, notably difficulties in accurately interpreting messages when faced with confrontational attitudes from colleagues or superiors. Additionally, some nurses exhibited asymmetrical communication patterns, prioritizing their own perspectives over others' during interactions. The findings revealed a statistically significant disparity between pre- and post-test scores (P = 0.017). The nurses’ mean score has improved by 5.14 after attending the four workshop coaching experience, passing from 5.71 in the pre-test to 10.85 in the post-test.

This is the first study in Morocco to evaluate the effectiveness of coaching workshops guided by a transtheoretical coaching model in improving communication skills and overcoming communication barriers among working emergency nurses.

Artificial intelligence (AI) allows the brand to co-create value with young customers through mobile apps. However, as many brands claim that their mobile apps are using the most updated AI technology, young customers face app fatigue and start questioning the authenticity of this touchpoint. This paper aims to study the mediating effect of authenticity for the value co-creation of AI-powered branded applications.

Drawing from regulatory engagement theory, this study conceptualize authenticity as the key construct in customers’ value experience process, which triggers customer value co-creation. Two scenario-based online experiments are conducted to collect data from 444 young customers. Data analysis is performed using ANOVA and Process Hayes.

The results reveal that perceived authenticity is an important mediator between media richness (chatbot vs AI text vs augmented reality) and value co-creation. There is no interaction effect of co-brand fit (high vs low) and source endorsement (doctor vs government) on the relationship between media richness and perceived authenticity, whereas injunctive norms (high vs low) strengthen this relationship.

The finding provides insights for marketing managers on engaging young customers suffering from app fatigue. Authenticity holds the key to young customers’ technological perceptions.

This research highlights the importance of perceived authenticity in encouraging young customers to co-create value. Young customers consider authenticity as a motivational force experience that involves customers through the app’s attributes (e.g. media richness) and social standards (e.g. norms), rather than brand factors (e.g. co-brand fit, source endorsement).

This study addresses the ever-increasing cyber risks confronting the global banking sector, particularly in India, amid rapid technological advancements. The purpose of this study is to de velop an innovative cyber fraud (CF) response system that effectively controls cyber threats, prioritizes fraud, detects early warning signs (EWS) and suggests mitigation measures.

The methodology involves a detailed literature review on fraud identification, assessment methods, prevention techniques and a theoretical model for fraud prevention. Machine learning-based data analysis, using self-organizing maps, is used to assess the severity of CF dynamically and in real-time.

Findings reveal the multifaceted nature of CF, emphasizing the need for tailored control measures and a shift from reactive to proactive mitigation. The study introduces a paradigm shift by viewing each CF as a unique “fraud event,” incorporating EWS as a proactive intervention. This innovative approach distinguishes the study, allowing for the efficient prioritization of CFs.

The practical implications of such a study lie in its potential to enhance the banking sector’s resilience to cyber threats, safeguarding stability, reputation and overall risk management.

The originality stems from proposing a comprehensive framework that combines machine learning, EWS and a proactive mitigation model, addressing critical gaps in existing cyber security systems.