Online information search by people with Multiple Sclerosis: A systematic review.

Background: People with Multiple Sclerosis (pwMS) search for information online about various aspects of living with their disease, but details about patterns of searching and outcomes are unclear. This means that opportunities to leverage online resources to support pwMS, and to enhance shared decision making, may be missed. We aimed to do a systematic review of the literature on digital information searching by pwMS.

Snakes and Ladders: Unpacking the Personalisation-Privacy Paradox in the Context of AI-Enabled Personalisation in the Physical Retail Environment.

Artificial intelligence (AI) is expected to bring to the physical retail environment the kind of mass personalisation that is already common in online commerce, delivering offers that are targeted to each customer, and that adapt to changes in the customer's context. However, factors related to the in-store environment, the small screen where the offer is delivered, and privacy concerns, create uncertainty regarding how customers might react to highly personalised offers that are delivered to their smartphones while they are in a store. To investigate how customers exposed to this type of AI-enabled, personalised offer, perceive it and respond to it, we use the personalisation-privacy paradox lens.

Stakeholders of the world, unite!: Hospitality in the time of COVID-19.

The COVID-19 pandemic created a global, complex crisis, without a clear end in sight, presenting an existential threat to many hospitality businesses. Drawing on stakeholder theory, we develop a framework for recovery strategy development for COVID-19, which engages salient stakeholders in the process of recognizing challenges, rationalizing changes needed and refashioning ways of working. The framework is used to analyze the process of development of a recovery strategy for a boutique hotel in England, UK, via a case study methodology.

Leveraging machine learning in the global fight against money laundering and terrorism financing: An affordances perspective.

Financial services organisations facilitate the movement of money worldwide, and keep records of their clients' identity and financial behaviour. As such, they have been enlisted by governments worldwide to assist with the detection and prevention of money laundering, which is a key tool in the fight to reduce crime and create sustainable economic development, corresponding to Goal 16 of the United Nations Sustainable Development Goals. In this paper, we investigate how the technical and contextual affordances of machine learning algorithms may enable these organisations to accomplish that task.